2016
4
2
0
209
Wind resource assessment of Khuzestan province in Iran
2
2
In this research paper, a 10 minute period measured wind speed data at 10 m, 30 m, and 40 m heights are presented for one of the major provinces of Iran. Four stations in Khuzestan Abadan, Hosseyneh, Mahshahr, and Shushtar are analyzed to determine the potential of wind power generation in this province. From the primary evaluation and by determining mean wind speed and also the Weibull function, the results show that the measurement site falls under class 2 of the International System Wind Classification for Abadan, Hosseyneh, and Mahshahr and class 1 for Shushtar station. It means that the first three stations have mediocre conditions for installing and operating wind farms, but Shushtar does not have a significant condition for connection to national power grid applications. By using wind roses of speed, turbulence, and the power distribution, the best direction of installing wind turbines for each station was determined. Finally, by utilizing power curves of five typical wind turbines, the annual wind energy, which is produced by a typical wind turbine for one of four stations, Mahshahr, was determined for showing the appropriate annual energy received from a wind turbine.
1

81
94


Pedram
Hanafizadeh
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 111554563
Center of Excellence in Design and Optimization
Iran
hanafizadeh@ut.ac.ir


Amirmohammad
Sattari
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 111554563
Center of Excellence in Design and Optimization
Iran
amirmsattari@ut.ac.ir


Seyed Erfan
Hosseinidoost
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 111554563
Center of Excellence in Design and Optimization
Iran
erfanhosseini@ut.ac.ir


Ashkan
Irannezhad
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 111554563
Center of Excellence in Design and Optimization
Iran
irannezhad@ut.ac.ir


Pouria
Ahmadi
Fuel Cell Research Lab (FCReL), Mechatronic System Engineering, Simon Fraser University, Vancouver, Canada
Fuel Cell Research Lab (FCReL), Mechatronic
Iran
pahmadi@sfu.ca
Weibull function
Wind energy
Wind roses
Wind speed
wind turbine
[[1] Wind Power, Encyclopaedia Britannica Encyclopaedia Britannica Ultimate Reference suite. Chicago: Encyclopaedia Britannica (2012). ##[2] http://www.powertechnology.com/ ##[3] Global Wind Energy Council, ## http://www.gwec.net/ ##[4] http://rredc.nrel.gov/wind/pubs/atlas/ ##[5] http://www.energy.eu/publications/ ##[6] http://www.irena.org/globalatlas/ ##[7] Renewable energies organization of Iran, http://suna.ir/ ##[8] Mostafaeipour A., Abarghooei H., Harnessing Wind Energy at Manjil Area Located in North of Iran, Renewable and Sustainable Energy Reviews (2008)12:17581766. ##[9] Mirhosseini M., Sharifi F., Sedaghat A., Assessing the Wind Energy Potential Locations in Province of Semnan in Iran”, Renewable and Sustainable Energy Reviews (2010)15: 449459. ##[10] Saeidi D., Mirhosseini M., Sedaghat A., Mostafaeipour A., Feasibility Study of Wind Energy Potential in Two Provinces of Iran: North and South Khorasan, Renewable and Sustainable Energy Reviews (2011)15: 3558–3569. ##[11] Alamdari P., Nematollahi O., Mirhosseini S., AlemRajabi A., Assessing the Wind Energy Potential Locations in Province of Ardabil Proceedings of the 1st International Conference on Emerging Trends in Energy Conservation  ETEC Tehran,Tehran, Iran, (2011) Nov. 2021. ##[12] Mostafaeipour A., Sedaghat A., DehghanNiri A.A., Kalantar V., Wind Energy Feasibility Study for City of Shahrbabak in Iran, Renewable and Sustainable Energy Reviews (2011) 15: 25452556. ##[13] www.thewindpower.net/countryen38iran.php ##[14] Neyestanak A.A.L., Wind Energy Developments in Manjil and Roodbar (Iran), Electrical Power Conference (EPC), IEEE Canada (2007) 336341. ##[15] http://iran.ahk.de/news/newsarchiv/details/artikel/windpowerexpanding ##[16] Wind Resource Assessment Handbook (NREL), Fundamental of Conducting a Successful Monitoring Program. AWS Scientific (1997). ##[17] Dan Reboussin, Wind Rose. University of Florida. Retrieved (2009) 04026. ##[18] Manwell J.F., MC Gowan J.G., Rogers A.L., Wind Energy Explained, Theory, Design and Application. Amherst, USA, John Wiley & Sons (2002). ##[19] Burton T., Sharpe D., Jenkines N., Bossanyi E., Wind Energy Handbook, John Wiley & Sons (2001). ##[20] Keyhani A., GhasemiVarnamkhasti M., Khanali M., Abbaszadeh R., An Assessment of Wind Energy Potential as a Power Generation Source in the Capital of Iran, Tehran. Energy (2010) 35(1):188201. ##[21] Jamil M., Parsa S., Majidi M., Wind Power Statistics and Evaluation of Wind Energy Density, Renew Energy (1995) 6:6238. ##[22] Renne D.S., , Wilcox S., Marion W., Gene L. Maxwell, Martin Rymes, Julie Phillips, Dale E. Berg, Marjorie A. Franklin, Robert C. Brown. Hand book of Energy Efficiency and Renewable Energy. Taylor & Francis Group, LLC; (2007). ##[23] Hoffmann R., A Comparison of Control Concepts for Wind Turbines in Terms of Energy Capture. Darmstadt (2002).##]
Experimental study of nanofluid effects on heat transfer in closed cycle system in shell and tube heat exchangers at Isfahan power plant
2
2
The present study investigated the effect of distilledwater/ silver nanofluid on heat transfer in a closed cooling system of one of the electrical energy generation units at Isfahan power plant. The difference between this study and previous researches refers to silver properties which have a high thermal conductivity and is nontoxic, hydrophilic and ecofriendly. Distilled water/ silver nanofluid with 20 nm average diameter and 0.1% volume fraction was purchased from Iranian nanomaterials’ Pishgaman company. The volume fractions 0.01%, 0.025%, 0.05%, 0.075% were prepared in the laboratory and thermophysical properties were practically measured in the laboratory. Nanofluids with flow rates from 0.14 to 0.26 kg/s and volume fraction from 0.01% to 0.1% passed through the tubes of heat exchanger and were evaluated in the Reynolds number range of 1500 to 4000. This study was aimed to achieve the overall heat transfer coefficients and pressure drop of the system. According to the system performance index, the results showed that nanofluid can be used to increase the efficiency of heat exchangers and, as an appropriate method, to reduce the dimensions of the exchangers. The results showed that nanofluid increased heat transfer coefficient by 16%. Pressure drop of nanofluid, however, have no significant change compared with the pressure drop of pure water.
1

95
109


Ali Reza
Yousefnejad
Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Department of Mechanical Engineering, Najafabad
Iran
alireza.ips@gmail.com


Mohammad Mahdi
Heyhat
Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
Faculty of Mechanical Engineering, Tarbiat
Iran
mmheyhat@modares.ac.ir


Amir Homayoun
Meghdadi
Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Department of Mechanical Engineering, Najafabad
Iran
amir_meghdadi@pmc.iaun.ac.ir
Closed System
Heat transfer coefficient
Nanofluid
Shell and Tube Heat Exchanger
silver nanoparticles
[[1] Farajollahi B., Etemad S.Gh., Hojjat M., Heat Transfer of Nanofluids in a Shell and Tube Heat Exchanger, International Journal of Heat and Mass Transfer (2010) 53: 12–1,. ##[2] Seifi Jamnani M., Hoseini S.M., Hashemabadi S.H., PeyGhambarzad S.M., Experimental Study of Al2O3/Water NanoFluid Flow Heat Transfer Through the Elliptical Tubes, ICHEC13, Kermanshah Iran (2010). ##[3] Peyghambarzadeh SM., Hashemabadi SH., Seifi Jamnani M., Hoseini SM.Improving the Cooling Performance of Automobile Radiator with Al2O3/ Water Nanofluid, Applied Thermal Engineering (2011)31:1833–8. ##[4] Zamzamian A., Oskouie SN., Doosthoseini A., Joneidi A., Pazouki M., Experimental Investigation of Forced Convective Heat Transfer Coefficient in Nanofluids of Al2O3/EG and CuO/ EG in a double pipe and plate heat Exchangers under Turbulent Flow, Experimental Thermal and Fluid Science (2011) 35(3):495–502. ##[5] Godson Asirvatham L., Raja B., Lal DM., Wongwises S., Convective Heat Transfer of Nanofluids with Correlations. Particuology (2011) 9:626–31. ##[6] Patel S., Patel V., Thakkar V., Experimental Investigation of Diameter Effect of Al2O3 Nano Fuid on Shell and Tube Heat Exchanger in Laminar Flow Regime (2015) 2(5): Online ISSN: 23939877, Print ISSN: 23942444. ##[7] Dharun Arvind R., Heat Transfer Analysis Of Shell And Tube Heat Exchanger Using Aluminium Nitride / Water Nanofluid", International Journal on Applications in Mechanical and Production Engineering (2015) 1: 1315. ##[8] Amani J., AbbasianArani A.A., Experimental Investigation of Diameter Effect on Heat Transfer Performance and Pressure Drop of TiO2 Water Nano Fluid, Experimental Thermal and Fluid Science (2013) 44: 520533. ##[9] Godson L., Deepak K., Enoch C., Heat Transfer Characteristics of Silver/Water Nano Fluids in a Shell and Tube Heat Exchanger, Archives of Civil and Mechanical Engineering (2014) 14: 489496. ##[10] Afshoon Y., Fakhar A., Numerical Study of Improvement in Heat Transfer Coefficient of CuO Water Nanofluid in the Shell and Tube Heat Exchangers, Biosciences Biotechnology Research Asia (2014) 11(2): 739747. ##[11] Baghmisheh Gh., Moradzadeh M., Hedayatzadeh S., Dorosti R., Industrial Heat Exchanger Design with ASPEN BJAC (2007) 6790. ##[12] Taylor J.R., An Introduction to Error Analysis, The Study of Uncertainties of Physical Measurements. University Science Books (1982). ##[13] Bork P.V., Grote H., Notz D., Regler M., Data Analysis Techniques in High Energy Physics Experiments. Cambridge University Press (1993). ##[14] AkhavanBehabadi M.A., Mohseni S.G., Najafi H., Ramazanzadeh H. Heat Transfer and Pressure Drop Characteristics of Forced Convective evaporation in horizontal tubes with Coiled Wire Inserts, International Communications in Heat and Mass Transfer.##]
Improvement of perturb and observe method for maximum power point tracking in wind energy conversion system using fuzzy controller
2
2
One of the main problems in wind energy conversion system (WECS) is how to achieve maximum output power in different wind speeds. Maximum methods for maximum power point tracking in wind energy conversion systems require the knowledge of system characteristics and mechanic sensors. So, using these methods practically will follow with high price and an abundant difficulties. In this paper, new method for maximum power point tracking based on fuzzy controller has been presented that is maximum power point tracking with high power coefficient without requiring mechanical sensors and knowing system characteristics. Wind energy conversion system is simulated by using tracking system based on fuzzy controller in MATLAB/SIMULINK and simulation results prove the advantages of suggested tracking method such as increase of power coefficient in wind turbine and decrease of fluctuations about maximum power point.
1

111
122


SeyedHadi
MozafarpoorKhoshrodi
Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran
Electrical Engineering, Najafabad Branch,
Iran
h_mozafarpoor@yahoo.com


Ghazanfar
Shahgholian
Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran
Electrical Engineering, Najafabad Branch,
Iran
shahgholian@iaun.ac.ir
Permanent Magnet Synchronous Generator (PMSG)
Maximum Power Point Tracking
Boost Converter
Fuzzy Controller
[[1]Wu Z.Q., Yang Y., Xu C.H., Adaptive Fault Diagnosis and Active Tolerant Control for Wind Energy Conversion System, International Journal of Control, Automation, and Systems (2015) 13 (1): 16. ##[2]Xia Y., Ahmed K.H., Williams B.W., A New Maximum Power Point Tracking Technique for Permanent Magnet Synchronous Generator Based Wind Energy Conversion System,IEEE Transactions on Power Electronics (2011) 26 (12): 3609–3620. ##[3]Tang Y., Bai Y., Huang C., Du B., Linear Active Disturbance RejectionBased Load Frequency Control Concerning High Penetration of Wind Energy, Energy Conversion and Management (2015) 95 (1): 259–271. ##[4]Hussein A.A., Ali M.H., Comparison Among Series Compensators for Transient Stability Enhancement of Doubly Fed Induction Generator Based Variable Speed Wind Turbines, IET Renewable Power Generation (2016) 10 (1): 116126. ##[5]Javaheri Fard H., Najafi H.R., Eliasi H., Active and Reactive Power Control Via Currents of a Rotor’s d and q Components with Nonlinear Predictive Control Strategy in a Doubly Fed Induction Generator Based on Wind Power System, Energy Equipment and Systems (2015) 3 (2): 143157. ##[6]Nejat A., Abyanaki M.R., Rahbari I., A Robust Engineering Approach for Wind Turbine Blade Profile Aeroelastic Computation, Energy Equipment and Systems (2014) 2 (2): 121128. ##[7]Mauricio J.M., Marano A., Eeposito A.G., Ramos J.L.M., Frequency Regulation Contribution Through Variable Speed Wind Energy Conversion System, IEEE Transactions on Power Systems (2009) 24 (1): 173180. ##[8]Zou Y., Elbuluk M., Sozer Y., Stability Analysis of Maximum Power Point Tracking (MPPT) Method in Wind Power Systems, Proceeding of the IEEE/IAS (2011) 18. ##[9]Kazmi S.M.R., Goto H., Guo H.J., Ichinokura O., A Novel Algorithm for Fast and Efficient SpeedSensorless Maximum Power Point Tracking in Wind Energy Conversion System, IEEE Transactions on Industrial Electron (2011) 58 (1): 29–36. ##[10] Shahgholian G., Izadpanahi N., Improving the Performance of Wind Turbine Equipped with DFIG Using STATCOM Based on InputOutput Feedback Linearization Controller, Energy Equipment and Systems (2016) 4 (1): 6579. ##[11]Nayanar V., Kumaresan N., AmmasaiGounden N., A SingleSensorBased MPPT Controller for WindDriven Induction Generators Supplying DC Mmicrogrid, IEEE Transactions on Power Electronics (2016) 31 (2): 11611172. ##[12]Linus R.M., Damodharan P., Maximum Power Point Tracking Method Using a Modified Perturb and Observe Algorithm for Grid Connected Wind Energy Conversion Systems, IET Renewable Power Generation (2015) 9 (6): 682689. ##[13]Dalala Z.M., Zahid Z.U., Yu W., Cho Y., Lai J.S., Design and Analysis of an MPPT Technique for SmallScale Wind Energy Conversion Systems, IEEE Transactions on Energy Conversion (2013) 28 (3): 756767. ##[14]Shaker M.S., Patton R.J., Active Sensor Fault Tolerant Output Feedback Tracking Control for Wind Turbine Systems Via T–S Model, Engineering Applications of Artificial Intelligence (2014) 34: 1–12. ##[15] Fooladgar M., RokRok E., Fani B., Shahgholian G, Evaluation of the Trajectory Sensitivity Analysis of the DFIG Control Parameters in Response to Changes in Wind Speed and the Line Impedance Connection to the Grid DFIG, Journal of Intelligent Procedures in Electrical Technology (2015) (20): 3754. ##[16] Lalouni S., Rekioua D., Idjdarene K., Tounzi A., Maximum Power Point Tracking Based Hybrid HillClimb Search Method Applied to Wind Energy Conversion System, Electric Power Components and Systems, (2015) 43 (810): 10281038 ##[17]Shin H.S., Xu C., Lee J.M., La J.D., Kim Y.S., MPPT Control Technique for a PMSG Wind Generation System by the Estimation of the Wind Speed, Proceeding of the IEEE/ICEMS (2012) 16. ##[18]Subudhi B., Ogeti P.S., Sliding Mode Approach to Torque and Pitch Control for a Wind Energy System, Proceeding of the IEEE/INDICON (2012) 244250. ##[19]Savio M., Sasikumar M., Space Vector Control Scheme of Three Level ZSI Applied to Wind Energy Systems, International Journal of Engineering (2012) 25 (4): 275282. ##[20]Datta R., Ranganathan V.T., A Method of Tracking the Peak Power Points for a Variable Speed Wind Energy Conversion System, IEEE Transactions on Energy Conversion (2003) 18 (1): 163168. ##[21]Koutroulis E., Kalaitzakis K., Design of a Maximum Power Tracking System for WindEnergyConversion Applications,IEEE Transaction on Industry Applications (2006) 53 (2): 486–494. ##[22]Soetedjo A., Lomi A., Mulayanto W.P., Modeling of Wind Energy System with MPPT control, Proceeding of the IEEE/ ICEEI (2011)16. ##[23]Zhensheng D.L., Wang W.H., Wang T., MPPT Control Strategy for offGrid Wind Power System, Proceeding of the IEEE/PEDG (2010) 759764. ##[24]Tan K., Islam S., Optimum Control Strategies in Energy Conversion of PMSG Wind Turbine System without Mechanical Sensors, IEEE Transaction on Energy Conversion (2004) 19 (2): 392399. ##[25]Mahdavian M., Wattanapongsakorn N., Shahgholian Gh., Mozafarpoor S.H., Janghorbani M., Shariatmadar S.M., Maximum Power Point Tracking in Wind Energy Conversion Systems using Tracking Control System Based on Fuzzy Controller, Proceeding of the IEEE/ECTICON, Nakhon Ratchasima, Thailand (2014). ##[26]Patsios C., Chaniotis A., Rotas M., Kladas A.G.,A Comparison of Maximum Power Point Tracking Control Techniques for Design of a Wind Energy Conversion System Including a Matrix Converter, Ph.D. Thesis, Waterloo, Ontario, Canada (2008). Low Power Variable Speed Wind Generators, EPE Chapter Electric Drives Joint Symposium, (2009). ##[27]Barakati S.M., Modeling and Controller Design of a Wind Energy Conversion System Including a Matrix Converter, Ph.D. Thesis, Waterloo, Ontario, Canada (2008). ##[28] Shahgholian G., Khani K., Moazzami M., Frequency Control in Autanamous Microgrid in the Presence of DFIG Based Wind Turbine, Journal of Intelligent Procedures in Electrical Technology (2015) 6 (23): 312. ##[29] Mesemanolis A., Mademlis C., Kioskeridis I., Maximum Efficiency of a Wind Energy Conversion System with a PM Synchronous Generator, Proceeding of the IEEE/MEDPOWER (2010) 19. ##[30] Faiz J., HakimiTehrani A., Shahgholian G., Current Control Techniques for Wind Turbines, A Review, Journal of Electromotion (2012) 19 (34): 151168. ##[31] Nadhir A., Hiyama T., Maximum Power Point Tracking Based Optimal Control Wind Energy Conversion System, Proceeding of the IEEE/ACT (2010) 4144. ##[32] Pan C.T., Juan Y.L., A Novel Sensorless MPPT Controller for a HighEfficiency Micro Scale Wind Power Generation System, IEEE Transactions on Energy Conversion (2010) 25 (1):207–216. ##[33] Jain B., Jain S., Nema R.K., Control Strategies of Grid Interfaced Wind Energy Conversion System, An Overview, Renewable and Sustainable Energy Reviews (2015) 47: 983–996 ##[34]Haque M.E., Negnevitsky M., Muttaqi K.M., A Novel Control Strategy for a VariableSpeed Wind Turbine with a PermanentMagnet Synchronous Generator, IEEE Transactions on Industry Applications (2010) 46 (1): 331339. ##[35]Sarvi M., Azarbara S., A Novel Maximum Power Point Tracking Method Based on Extension Theory for Wind Energy Conversion System, International Journal of Computer Science and Engineering Technology (2012) 3 (8): 294303. ##[36]Chen Z., Guerrero J.M., Blaabjerg F., A Review of the State of the Art of Power Electronics for Wind Turbines, IEEE Transactions on Power Electronics (2009) 24 (8): 18591875. ##[37]Qiao W., Yang X., Gong X., Wind Speed and Rotor Position Sensorless Control for DirectDrive PMG Wind Turbines, IEEE Transactions on Industry Applications (2012) 48 (1): 311. ##[38]Chinchilla M., Amaltes S., Burgos J.C., Control of Permanent Magnet Generators Applied to VariableSpeed WindEnergy Systems Connected to the Grid, IEEE Transactions on Energy Conversion (2006) 21 (1): 130–135. ##[39]Blaabjerg F., Liserre M., Ma K., Power Alectronics Converters for Wind Turbine Systems, IEEE Transactions on Energy Conversion (2011) 23 (1):257264. ##[40]Elnaggar M.M., Fattah H.A.A., Elshafei A.L., Numerical Optimization Algorithm for Maximum Power Point Tracking in Wind Energy Conversion System, Proceeding of the IEEE/CCA, (2012) 806811.##]
Theoretical and experimental investigation into incident radiation on solar conical collector
2
2
The geometry of a collector is one of the important factors that can increase the incident radiation on the collector surface. In the present study, the incident radiation for a stationary collector with cone geometry, i.e. a conical collector, is theoretically and experimentally investigated. This type of collector is always stable and does not need a fixture to install. Moreover, it has a symmetric geometry, with all its sides facing the sun. The main advantage of this collector is its ability to receive beam, diffuse, and groundreflected radiation throughout the day. The variation of the incident radiation is theoretically estimated by using an isotropic sky model based on the available data. The theoretical data are validated by an experimental test of a conical collector of a specific size. The results show that the conical solar collector is more operative in receiving total solar radiations than a horizontal plate such as a flatplate collector and can be a suitable option for solar water heating. A calculation of the incident radiation shows that the incident radiation is maximized when the cone angle of the conical collector is equal to the latitude of the site test.
1

123
132


Amin Reza
Noghrehabadi
Mechanical Engineering Department, Faculty of Engineering, Shahid Chamran University, Ahvaz, Iran
Mechanical Engineering Department, Faculty
Iran


Farshad
Torabi
Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Faculty of Mechanical Engineering, K. N.
Iran
ftorabi@kntu.ac.ir


Ebrahim
Hajidavaloo
Mechanical Engineering Department, Faculty of Engineering, Shahid Chamran University, Ahvaz, Iran
Mechanical Engineering Department, Faculty
Iran
hajidae@scu.ac.ir


Mojtaba
Moravej
Mechanical Engineering Department, Faculty of Engineering, Shahid Chamran University, Ahvaz, Iran
Mechanical Engineering Department, Faculty
Iran
moravej60@gmail.com
Incident Radiation
Solar Collector
Conical Collector
Experimental Investigation
Theoretical Investigation
Isotropic Sky Model
[[1] Kalogirou S., Solar Thermal Collectors and Applications, Prog Energy Combust Sci, 30: 231295. ##[2] Duffie J.A, Beckman W.A., Solar Engineering of Thermal Processes, 4nd ed, New York, Wiley (2013). ##[3] Motte F., Notton G., Cristofari C., Canaletti J., Design and Modeling of a New Patented Thermal Solar Collector with Building Integration, Appl Energy (2013) 102: 631639. ##[4]Katiyar A.K., Kumar A., Pandey CK., Katiyar VK., Abdi SH., Correlations for the Estimation of Monthly Mean Hourly Diffuse Solar Radiation, a Time Dependent Approach, The International Journal of Energy and Environment. (2010) 1(5):83340. ##[5] Karsli S., Performance Analysis of a NewDesign Solar Air Collector for Drying Applications, Renew Energy (2007) 32:16451660. ##[6] Samanta B., Khamis Rajab AI Balushi. Estimation of Incident Radiation on a Novel Spherical Solar Collector, Renew Energy (1998) 14: 241247. ##[7] AlSulaiman F.A, Ismaili B., Estimation of Solar Radiation Impinging on a Sloped Surface Using Isotropic Sky Model for Dhahran, Saudi Arabia, Renew Energy (1997)11: 257262. ##[8] Pelece I., Iljins U., Ziemelis I., Theoretical Calculation of Energy Received by SemiSpherical Solar Collector, Argon, Res (2008) 6: 263269. ##[9] Pelece I., Ziemelis I., Iljins U., Surface Temperature Distribution and Energy Gain from SemiSpherical Solar Collector. Proceeding of World Renewable Energy Congress, Linkoping, Sweden (2011) 39133920. ##[10] Gaspar F., Balan M., Jantschi L., Ros V., Evaluation of Global Solar Radiation Received by a Spherical Collector. Bulletin UASVM Agriculture (2012) 69: 128135. ##[11] Kumar N., Chavda T., Mistry H. N., A Truncated Pyramid Non Tracking Type Multipurpose Solar Cooker/hot Water System, Applied Energy (2010) 87: 471477. ##[12] Tian Y., Zhao C. Y., A Review of Solar Collectors and Thermal Energy Storage in Solar Thermal Applications, Applied Energy (2013) 104: 538553. ##[13]Bannerot R. B, Howell J. R., Predicted Daily and Yearly Average Radiation Performance of Optimal Trapezoidal Groove Solar Energy Collectors. Solar Energy (1979) 22: 229. ##[14] Smyth M., Zacharopoulos A., Eames P. C., Norton B., An Experimental Procedure to Determine Solar Energy Flux Distributions on the Absorber of LineAxis Compound Parabolic Concentrators, Renew Energy (1999) 16: 761764. ##[15] Erbs D. G., Klein S. A., Duffie J. A., Estimation of the Diffuse Radiation Fraction for Hourly, Daily and MonthlyAverage Global Radiation, Solar Energy (1982) 28: 293. ##[16] Gueymard C., Interdisciplinary Applications of a Versatile Spectral Solar Irradiance Model, a Review, Energy (2005) 30: 1551. ##[17] Hay J. E., McKay D. C., Estimating Solar Irradiance on Inclined Surfaces, a Review and Assessment Methodologies. Intertional Journal Solar Energy (1985) 3: 203. ##[18] Hottel H. C., A Simple Model for Estimating the Transmittance of Direct Solar Radiation Through Clear Atmospheres. Solar Energy (1976) 18: 129. ##[19]Souliotis M., Tripanagnostopoulos Y., Study of the Distribution of the Absorbed Solar Radiation on Theperformance of a CPCtype ICS Water Heater, Renew Energy (2008) 33:846–858. ##[20]Klein S. A., Theilacker J. C., An Algorithm for Calculation MonthlyAverage Radiation on Inclined Surfaces. ASME Journal Solar Energy Engineering (1981) 103: 29. ##[21] Knight K. M., Klein S. A., Duffie J. A., A Methodology for Synthesis Hourly Weather Data, Solar Energy (1991) 46: 109. ##[22] Perez R., Ineichen P., Seals R., Michalsky J., Stewart R., Modeling Daylight Availability and Irradiance Components from Direct and Global Irradiance, Solar Energy (1990) 44: 271289. ##[23] Skartveit A., Olseth J. A., A Model for Diffuse Fraction of Hourly Global Radiation, Solar Energy (1987) 38: 271274. ##[24] Soulayman S. SH., On the Optimum Tilt of Solar Absorber Plates, Renew Energy (1991) 1: 551554. ##[25] Morcos V. H., Optimum Tilt Angle and Orientation for Solar Collectors in Assiut, Egypt, Renew Energy (1994) 4: 191202.##]
Aerodynamic optimization of a 5 Megawatt wind turbine blade
2
2
Wind power has been widely considered in recent years as an available and a clean renewable energy source. The cost of wind energy production is currently the main issue, and increasing the size of wind turbines can reduce wind energy production costs. Hence, megawatt wind turbines are being rapidly developed in recent years. In this paper, an aerodynamic analysis of the NREL 5MW turbine is carried out using the modified blade element momentum theory (BEM). The genetic algorithm (GA) as an optimization method and the Bezier curve as a geometry parameterization technique are used to optimize the original design. The modified BEM results are compared with the NREL published results for verification. Cost of energy (COE) is considered an objective function, which is one of the most important and common choices of objective function for a megawatt wind turbine. Besides, the optimization variables involve chord and twist distributions variation along the blade span. The optimal blade shape is investigated for the minimum cost of energy with considered constant rotor diameter and airfoil profiles. Then the objective function is improved and a new optimum geometry is compared with the original geometry. Although the Annual Energy Production and rated power are reduced by 2% and 3% respectively, the net cost of wind energy production is decreased by 15%, showing the importance of such optimization studies.
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133
145


Hamid
Moradtabrizi
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Mechanical Engineering, College
Iran
h_mtabrizi@ut.ac.ir


Edris
Bagheri
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Mechanical Engineering, College
Iran


Amir
Nejat
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Mechanical Engineering, College
Iran
nejat@ut.ac.ir


Hamid
Kaviani
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Mechanical Engineering, College
Iran
hamid_kaviani@ut.ac.ir
Aerodynamic Optimization
Megawatt Wind Turbine
Blade Element Momentum Theory (BEM)
Genetic Algorithm (GA)
Bezier curve
Cost Of Energy (COE)
[[1] Brøndsted P., Rogier P., Nijssen P., Advances in Wind Turbine Blade Design and Materials, Woodhead Publishing Series in Energy(2013) 47. ##[2] SANT T., Improving BEM‐based Aerodynamic Models in Wind Turbine Design Codes. Doctoral thesis; University of Malt (2007). ##[3] Amano R.S., Malloy R.J., CFD Analysis on Aerodynamic Design Optimization of Wind Turbine Rotor Blades. World Academy of Science, Engineering and Technology (2009) 36. ##[4] Mukesh Y., Anjuri M., EswaraRao W., CFD Predictions of NREL Phase VI Rotor Experiments in NASA/AMES Wind tunnel. International Journal of Renewable Energy Research (2013). ##[5] Sedaghat A., Mirhosseini M., Moghimi Zand M., Aerodynamic Design and Economical Evaluation of Site Specific Horizontal Axis Wind Turbine (HAWT), Energy Equipment and Systems (2014). ##[6] Bumsuk K., Woojun K., Sungyoul B., Jaehyung P., Manneung K., Aerodynamic Design and Performance Analysis of MultiMW Class Wind Turbine Blade, Mechanical Science and Technology, Seoul, Korea (2011). ##[7] Benini E., Toffolo A., Optimal Design of HorizontalAxis Wind Turbines Using BladeElement Theory and Evolutionary Computation, Journal Solar Energy – Trans ASME (2002)124:357–63. ##[8] Yang H., Prediction of the Wind Turbine Performance by Using BEM with Airfoil Data Extracted From CFD, Renewable Energy (2014). ##[9] Rathore A.S., Ahmed S., Aerodynamic Analyses of Horizontal Axis Wind Turbine By Different Blade Airfoil Using Computer Program, IOSR Journal of Engineering (IOSRJEN) (2012) 2:118123. ##[10] Yassin K., Diab A., Ghoneim Z., Aerodynamic Optimization of a Wind Turbine Blade Designed for Egypt's Saharan Environment Using a Genetic Algorithm, Journal of Renewable Energy and Sustainable Development (RESD) (2015). ##[11] Ceyhan O., Aerodynamic Design and Optimization of Horizontal Axis Wind Turbines by Using BEM Theory and Genetic Algorithm, Master Thesis, Aerospace Engineering Department, METU, Ankara (2008). ##[12] Ceyhan O., Sezer Uzol N., Tuncer I., Optimization of Horizontal Axis Wind Turbines by Using BEM Theory and Genetic Algorithm, In: Proceedingsof the 5th Ankara International Aerospace Conference. METU, Ankara, Turkey (2009). ##[13] Liu X., Chen Y., Ye Z., Optimization Model for Rotor Blades of Horizontal Axis Wind Turbines, Frontiers of Mechanical Engineering in China (2007) 2(4): 483–488. ##[14] Burton T., Sharpe D., Jenkins N., Bossanyi E., Wind Energy Handbook, John Wiley & Sons (2001). ##[15] Manwell J.F., McGowan J.G., Rogers A.L., Wind Energy Explained, Theory Design and Application, John Wiley& Sons Incorporated (2010). ##[16] Hansen M.O.L., Aerodynamics of Wind Turbines, 2nd Edition, Earth Scan Publications (2008). ##[17] Glauert H., The Elements of Aerofoil Theory and Airscrew Theory, Cambridge University Press (1959). ##[18] Xudong W., Shen W.Z., Zhu W.J., Sorensen N., Shape Optimization of Wind Turbine Blades, Wind Energy (2009)12: 781803. ##[19] Brand A.J., Offshore Wind Atlas of the Dutch Part of the North Sea, Energy Research Centre of the Netherlands, NL (2008). ##[20] Jonkman J., Buttefield S., Musial W., Scott G., Definition of a 5MW Reference Wind Turbine for Offshore System Development, Technical Report National Renewable Energy Laboratory (2009). ##[21] Jarquin A., Steady State Performance of the Delft Offshore Turbine, Master of Science Thesis, Delft University of Technology (2010).##]
Multiobjective optimization of compression refrigeration cycle of Unit 132 South Pars refineries
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2
The purpose of this paper is multiobjective optimization of refrigeration cycle by optimization of all components of the cycle contains heat exchangers, air condenser, evaporator and superheater. Studied refrigeration cycle is compression refrigeration cycle of unit 132 Third refineries in south pars that provide chilled water for cooling refinery equipment's. Cycle will be performed by the genetic algorithm optimization. Thermodynamic purpose of the cycle Expressed by minimization of Exergy destruction or maximization or coefficient of performance (C.O.P), economic purpose of the cycle Expressed by minimization of cold water production cost by TRR method and environmental purpose of the cycle Expressed by minimization of NOx, CO2 and CO Which is produced by power consumption. Combination of objectives and decision variables with suitable engineering and physical constraints makes a set of the MINLP optimization problem. In EES software. Optimization programming is performed using NSGAII algorithm. Four optimization scenarios including the thermodynamic singleobjective, the economic singleobjective, environmental singleobjective by power electricity consumption and multiobjective optimizations are performed. The output of the multiobjective optimization is a Pareto frontier that yields a set of optimal points that the final optimal solution has been selected using two decisionmaking approaches including the LINMAP and TOPSIS methods.. It was shown that the best results in comparison to the simple cycle reduction in Exergy destruction from 264.8 kW to 127.6 kW(Increased coefficient of performance from 3.872 to 7.088), reduction in cold water production cost from 117.5 dollar/hour to 87.19 dollar/hour and reduction in NOx emission from 4958 kg/year to 2645 kg/year.
1

147
160


Ali Reza
Sheibani Tezerji
Mechanical Engineering Department, Islamic Azad University, Kerman Branch, Iran
Mechanical Engineering Department, Islamic
Iran


Mohammad Mehdi
Keshtkar
Mechanical Engineering Department, Islamic Azad University, Kerman Branch, Iran
Mechanical Engineering Department, Islamic
Iran
Compression Refrigeration Cycle
multiobjective optimization
Genetic Algorithm (GA)
TOPSIS and LINMAP DecisionMaking
[[1] Bejan A., Tsatsaronis G., Moran M., Thermal Design and Optimization, Journal Wiley (1996). ##[2] Leidenfrost W., Lee K.H., Korenic K.H., Conservation of Energy Estimated by Second Law Analysis of PowerConsuming Process, Energy (1980) 5:4761. ##[3] Dincer I., Edin M., Ture E., Investigation of Thermal Performance of a Solar Powered Absorption Refrigeration System, Energy Conversation & Management (1996) 37 (1): 5158. ##[4] Meunier F., Poyelle F., LeVan M.D., SecondLaw Analysis of Adsorptive Refrigeration Cycles, The Role of Thermal Coupling Entropy Production, Applied Thermal Engineering (1997)17 (1): 4355. ##[5] Nikolaidis C., Probert D., ExergyMethod Analysis of a Twostage VapourCompression RefrigerationPlants Performance, Applied Energy (1998)60: 241256. ##[6] Bouronis M., Nogues M., Boer D., Coronas A., Industrial Heat Recovery by Absorption/Compression Heat Pump Using TFEH2OTEGDME Working Mixture, Applied Thermal Engineering (2000)20: 355369. ##[7] Go¨ktun S., Yavuz H., Performance of Irreversible Combined Cycles for Cryogenic Refrigeration, Energy Conversation & Management (2000)41: 449459. ##[8] Chen J., Chen X., Wu C., Optimization of the Rate of Exergy Output of a Multistage Endoreversible Combined Refrigeration System, Exergy (2001)1 (2): 100106. ##[9] Kanoglu M., Exergy Analysis of the Multistage Cascade Refrigeration Cycle Used for Natural Gas Liquefaction, International Journal Energy Research (2002)26: 763774. ##[10] Yumrutas R., Kunduz M., Kanoglu M., Exergy Analysis of Vapour Compression Refrigeration Systems, Exergy International Journal (2002)2: 266272. ##[11] Kanoglu M., Carpınlıoglu M.O., Yıldırım M., Energy and Exergy Analyses of an Experimental OpenCycle Desiccant Cooling System. Applied Thermal Engineering (2004) 24: 919932. ##[12]Kopac M., Zemher B., Effect of SaturationTemperature on the Performance of a VapourCompression RefrigerationCycle Working on Different Refrigerants Using Exergy Method, International Journal Energy Research, (2006)30: 729740. ##[13]Ozgener O., Hepbasli A., A Review on the Energy and Exergy Analysis of Solar Assisted Heat Pump Systems, Renewable & Sustainable Energy Reviews (2007) 11: 482496. ##[14]Deb K., MultiObjective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Incorporation, New York, NY, USA (2001) ISBN 047187339X. ##[15]Dincer I., Edin M., Ture E., Investigation of Thermal Performance of a Solar Powered Absorption Refrigeration System, Energy Conversation & Management (1996)37 (1): 5158. ##[16]Sanaye S., Malekmohammadi H.R., Thermal and Economical Optimization of Air Conditioning Units with Vapor Compression Refrigeration Applied Thermal Engineering (2004)24: 18071825. ##[17]Selbas R., Kızılkan O., Sencana A., Economic Optimization of Subcooled and Superheated Vapor Compression Refrigeration Cycle, Energy (2006)31: 21082128. ##[18]Misra R.D., Sahoo P.K., Sahoo S., Gupta A., Thermoeconomic Optimization of a Single Effect Water/LiBr Vapour Absorption Refrigeration System, International Journal Refrigeration (2003)26 (2): 158169. ##[19]Sanaye S., Niroomand B., ThermalEconomic Modeling and Optimization of Vertical GroundCoupled Heat Pump, Energy Conversation Management (2009)50, 11361147. ##[20]Fonseca C.M., Fleming P.J., Multiobjective Optimization. In, Back T., Fogel D.B., Michalewicz Z. (Eds.), Handbook of Evolutionary Computation. Oxford University Press (1997). ##[21]Van Veldhuizen D.A., Lamont G.B., Multiobjective Volutionary Algorithms, Analyzing the Stateoftheart, Evolutionary Computation (2000)8 (2): 125147. ##[22]Deb K., MultiObjective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Incorporation New York, NY, USA (2001) ISBN 047187339X. ##[23]Konak A., Coit D.W., Smith A.E., MultiObjective Optimization Using Genetic Algorithms, A Tutorial Reliability Engineering and System Safety (2006) 91: 9921007. ##[24]Toffolo A., Lazzaretto A., Evolutionary Algorithms for MultiObjective Energetic and Economic Optimization in Thermal System Design, Energy (2002)27: 549567. ##[25]Toffolo A., Lazzaretto A., Energy, Economy and Environment as Objectives in MultiCriteria Optimization of Thermal System Design, Energy (2004)29: 11391157. ##[26]Sayyaadi H., Amlashi E.H., Amidpour M., MultiObjective Optimization of a Vertical Ground Source Heat Pump Using Evolutionary Algorithm. Energy Conversation Management (2009) 50: 20352046. ##[27]Sayyaadi H., Amlashi E.H., Various Criteria in Optimization of a Geothermal Air Conditioning System with a Horizontal Ground Heat Exchanger, International Journal of Energy Research (2010) 34: 233248. ##[28]Sayyaadi H., Nejatolahi M., MultiObjective Optimization of a Cooling Tower Assisted Vapor Compression Refrigeration System, International Journal of Refrigeration, International Journal of Refrigeration 341 (2010) 243256. ##[29]Yu P.L., MultipleCriteria Decision Making, Concepts, Techniques, and Extensions, Plenum Press, New York (1985). ##[30]Olson D.L., Decision Aids for Selection Problems, Springer, New York (1996). ##[31]Coulson J.M., Richardson J.F., Chemical Engineering Design, Third Edition, Butterworth and Heinemann (1996) 6. ##[32]Ludwing E.E., Applied Process Design for Chemical and Petrochemical Plants, Third Edition (1993)2. ##[33]Valero A., CGAM Problem, Definition and Conventional Solution, Energy (1994)19: 268279. ##[34]Selbas R., Kızılkan O., Sencana A., Economic Optimization of Subcooled and Superheated Vapor Compression Refrigeration Cycle Energy (2006)31: 21082128. ##[35]Iran Energy Balance Sheet. ##[36]Fonseca C. M., Fleming P. J., Multiobjective Optimization. In, Back T., Fogel D. B., Michalewicz Z., Handbook of Evolutionary Computation, Oxford University Press (1997). ##[37]Carvalho M.B., Ekel P. Ya, Martins C.A.P.S., Pereira J. G. Fuzzy SetBased Multiobjective Allocation of Resources, Solution Algorithms and Applications, Nonlinear Analysis (2005) 63: 715 – 724.##]
Development of a laboratory system to investigate and store electrical energy from the vibrations of a piezoelectric beam
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2
Energy harvesting from surrounding environment has been attractive for many researchers in recent years. Therefore, developing appropriate test apparatus to study energy harvesting mechanisms and their performance is of paramount importance. Due to their electromechanical characteristics, piezoelectric materials are used for harvesting energy from environmental vibrations. For optimum utilization of this system in harvesting and storing energy, the studies need to consider the environmental conditions. In this work, the electromechanical system is developed with the aim of conducting tests on piezoelectric materials. It is an integrated system which is developed and built after considering the limitations and sensitivity of piezoelectric material. In this research, the simple piezoelectric beam is also tested. Evaluation results via this system are analysed using Abaqus. The error value in receiving output voltage is 6% because an ideal open circuit state is considered by this software.
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161
168


Kamal
Jahani
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University
Iran


Mir. Meysam
Rafiei
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University
Iran


Reza
Aghazadeh Ayoubi
School of Industrial and Information Engineering, Polytechnic University of Milan, Milan, Italy
School of Industrial and Information Engineering,
Iran
Energy Harvesting
Piezoelectric Beam
Vibration
Laboratory System
Abaqus
[[1] Tiersten H.F., Linear Piezoelectric Plate Vibrations, Plenum Press, New York (1969). ##[2] Kymissis J. J., Kendall C., Paradiso J., Gershenfeld N., Parasiti Power Harvesting in Shoes, Second International Symposium on Wearable Computers, (1998)132139. ##[3] Oh S. J., Han H. J., Han S. B., Lee J. Y., Chun W. G., Development of a TreeShaped Wind Power System Using Piezoelectric Materials, International Journal of Energy Research(2010) 34(5): 431437. ##[4] Granstrom J., Feenstra J., Sodano H. A.,Farinholt K., Energy Harvesting From a Backpack Instrumented with Piezoelectric Shoulder Straps, Smart Materials and Structures (2007) 16( 5):18101820. ##[5] Lu F., Lee H.P., Lim S.P., Modeling and Analysis of Micro Piezoelectric Power Generators FormicroElectromechanicalSystems Applications. Smart Materials and Structures (2004)13, 57–63. ##[6] Chen S.N., Wang G.J., Chien M.C. Analytical Modeling of Piezoelectric VibrationInduced Micro Power Generator Mechatronics (2006) 16: 397–387. ##[7] Lin J.H., Wu X.M., Ren T.L., Liu L.T. Modeling and Simulation of Piezoelectric MEMS Energy Harvesting Device, Integrated Ferroelectrics (2007) 95: 128–141. ##[8] Erturk A. Inman D.J., Issues in Mathematical Modeling of Piezoelectric Energy Harvesters, Smart Materials and Structures (2008) 17: 065016. ##[9] Shu Y.C., Lien I.C., Analysis of Power Outputs for Piezoelectric Energy Harvesting Systems, Smart Materials and Structures (2006) 15: 1499–1502. ##[10] Guan M. J. Liao W. H., Characteristics of Energy Storage Devices in Piezoelectric Energy Harvesting Systems, Journal of Intelligent Material Systems and Structures (2008) 19(6):671680. ##[11] Kwon D., RinconMora G. A., A RectifierFree Piezoelectric Energy Harvester Circuit, IEEE International Symposium on Circuits and Systems (ISCAS) (2009)10851088. ##[12] Sodano H., Anton S., A Review of Power Harvesting Using Piezoelectric Materials, Journal of Intelligent Material Systems and Structures(2007). ##[13]Erturk A., Inman D. J., Piezoelectric Energy Harvesting, Fifth Edition, John Wiley & Sons Ltd, United Kingdom (2011). ##[14] CookChennault K.A., Thambi N., Sastry A.M. Powering MEMS Portable Devices, A Review of NonRegenerative and Regenerative Power Supply Systems with Emphasis on Piezoelectric Energy Harvesting Systems, Smart Materials and Structures (2008) 17: 043001.##]
Energy use efficiency, GHG emissions, and carbon efficiency of paddy rice production in Iran
2
2
The energy efficiency, greenhouse gas (GHG) emissions, and carbon efficiency of paddy rice production were analysed in Sari in the Mazandaran province of Iran during 2011–2012. Data was collected through questionnaires and interviews with paddy producers. The results showed that the net energy gain was 27,932 MJ ha1 and energy efficiency was 1.83 during production. The results of the CobbDouglas (CD) model showed that the energy inputs of machinery, diesel fuel, chemical fertilizers, and biocides had positive impacts on yield, while the impacts of seed and human labour were negative. For every 1 MJ increase in energy input, the inputs of seed, labour, machinery, diesel fuel, chemical fertilizers and biocides, changed the yield as 0.058, 0.992, 0.078, 0.004, 0.027, and 0.089 kg, respectively. The energy input of machinery with a high beta coefficient (0.64) had the most impact on crop yield (p≤0.01). The total GHG emission for paddy production was determined to be 1,936 kgCO2eq ha1, with diesel fuel and machinery having the greatest contributions.Carbon efficiency was estimated to be 4.01.
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169
176


Saeed
Firouzi
Department of Agronomy, College of Agriculture, Rasht Branch, Islamic Azad University, Rasht, Iran
Department of Agronomy, College of Agriculture,
Iran
firouzi@iaurasht.ac.ir


Amin
Nikkhah
Young Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran
Young Researchers and Elite Club, Rasht Branch,
Iran
amin.nikkhah@stu.um.ac.ir


Mehdi
Khojastehpour
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Biosystems Engineering, Ferdowsi
Iran
mkhpour@um.ac.ir


Nicholas
M. Holden
UCD School of Biosystems Engineering, Agriculture and Food Science Centre, University College Dublin, Belfield, Dublin, 4, Ireland
UCD School of Biosystems Engineering, Agriculture
Ireland
Diesel Fuel
Environmental Management
Sensitivity analysis
CobbDouglas Model
[[1] Anonymous, Food and Agriculture Organization of the United Nations (FAO), Fao Statistical Yearbook, Available on the FAO website (www.fao.org/publications) (2013). ##[2] Dyer J.A., Desjardins R.L., Simulated Farm Fieldwork, Energy Consumption and Related Greenhouse Gas Emissions in Canada, Biosystems Engineering (2003)85: 503513. ##[3] Dyer J.A., Desjardins R.L., Carbon Dioxide Emissions Associated with the Manufacturing of Tractors and Farm Machinery in Canada, Biosystems Engineering (2006) 93: 107118. ##[4] Hemmati A., Tabatabaeefar A.,Mousaviavval SH., Poozesh M.,. Energy Flow Modeling and Economic Analysis of Olive Production Based on Different Orchard size in Guilan Province of Iran, International Journal of Agriculture and Crop Sciences (2013). ##[5] Khoshnevisan B., Rafiee S., Omid M., Yousefi M., Movahedi M., Modeling of Energy ConSumption and GHG (greenhouse gas) Emissions in Wheat Production in Esfahan Province of Iran Using Artificial Neural Networks, Energy, (2013)52: 333338, 59(0): 6371. ##[6] Lal R., Carbon Emission From Farm Operations, Environment International (2004)30: 981990. ##[7] Liang S., Xu M., Zhang T., Life Cycle Assessment of Biodiesel Production in China, Bioresource Technology (2013)129(0): 7277. ##[8] MAJ, Ministry of JihadeAgriculture of Iran, Annual Agricultural Statistics, Available at: http://www.maj.ir (In Persian) (2011). ##[9] Mobtaker H.G., Keyhani A., Mohammadi A., Rafiee S., Akram A., Sensitivity Analysis of Energy Inputs for Barley Production in Hamedan Province of Iran, Agriculture, Ecosystems & Environment (2010) 137: 367372. ##[10] Mobtaker H.G., Akram A., Keyhani A., Energy Use and Sensitivity Analysis of Energy Inputs for Alfalfa Production in Iran, Energy for Sustainable Development (2012) 16: 8489. ##[11] Mohammadi A., Rafiee S., Mohtasebi S.S., Rafiee H., Energy Inputs – Yield Relationship and Cost Analysis of Kiwifruit Production in Iran, Renewable Energy (2010)35: 10711075. ##[12] Nasirahmadi A., Abbaspour Fard M.H., Emadi Behroozi B., Khazaei N., Modelling and Analysis of Compressive Strength Properties of Parboiled Paddy and Milled Rice, International Agrophys, (2014)28: 7383. ##[13] Nassiri S.M., Singh S., Study on Energy Use Efficiency for Paddy Crop Using Data Envelopment Analysis (DEA) Technique, Applied Energy (2009)86: 1320–1325. ##[14] Nikkhah A., Emadi B., Khojastehpour M., Payman S.H., HamzehKalkenari H., Invastigating the Energy Consumption of Peanut Production in Guilan Province Using Fuzzy Data Envelopment Analysis Method, The 8th National Congress on Agriculture Machinery Engineering (Biosystem) & Mechanization, 2931 January, Mashhad, Iran (In Farsi)(2014a). ##[15] Nikkhah A., HamzehKalkenari H., Emadi B., Shabanian F., Investigating the Relationship Between Energy Inputs and Yield of Tea in Guilan Province, The 8th National Congress on Agricultural Machinery Engineering (Biosystem) & Mechanization, 2931 January, Mashhad, Iran (In Farsi) (2014b). ##[16] Ozkan B., Akcaoz H., Fert C., Energy Input–Output Analysis in Turkish Agriculture, Renewable Energy (2004) 29: 3951. ##[17] Ozkan B., Ceylan R.F., Kizilay H., Comparison of Energy Inputs in Glasshouse Double Crop (Fall and Summer Crops) Tomato Production, Renewable Energy (2011)36: 16391644. ##[18] PishgarKomleh S.H., Sefeedpari P., Rafiee S., Energy and Economic Analysis of Rice Production under Different Farm Levels in Guilan Province of Iran, Energy (2011a)36: 58245831. ##[19] PishgarKomleh S.H., Keyhani A., Rafiee S., Sefeedpary P., Energy Use and Economic Analysis of Corn Silage Production under Three Cultivated Area Levels in Tehran Province of Iran, Energy (2011b) 36: 33353341. ##[20] PishgarKomleh S.H., Omid M., Heidari M.D., On the Study of Energy Use and GHG (greenhouse gas) Emissions in Greenhouse Cucumber Production in Yazd (2013). ##[21] PishgarKomleh S.H., Sefeedpari P., Ghahderijani M., Exploring Energy Consumption and CO[sub 2] Emission of Cotton Production in Iran, Journal of Renewable and Sustainable Energy (2012)4: 033115033114. ##[22] Rafiee S., MousaviAvval S.H., Mohammadi A., Modeling and Sensitivity Analysis of Energy Inputs for Apple Production in Iran, Energy (2010) 35: 33013306. ##[23] Ramedani Z., Rafiee S., Heidari M.D., An Investigation on Energy Consumption and Sensitivity Analysis of Soybean Production Farms, Energy (2011)36: 63406344. ##[24] Royan M., Khojastehpour M., Emadi B., Mobtaker H.G., Investigation of Energy Inputs for Peach Production Using Sensitivity Analysis in Iran, Energy Conversion and Management (2012)64: 441446. ##[25] Salehi M., Ebrahimi R., Maleki A., Ghasemi M.H., An Assessment of Energy Modeling and Input Costs for Green House Button Mushroom Production in Iran, Journal of Cleaner Productiom (2013). ##[26] Samavatean, N., Rafiee, S. Mobli H., Mohammadi A., An Analysis of Energy Use and Relation between Energy Inputs and Yield, Costs and Income of Garlic Production in Iran, Renewable Energy (2011)36: 18081813. ##[27] Singh S., Mittal J.P., Energy in Production Agriculture. Mittal Publications (1992). ##[28] Singh S., Singh S., Mittal J. P., Pannu C. J. S., Bhangoo B. S., Energy Inputs and Crop Yield Relationships for Rice in Punjab, Energy (1994)19: 10611065. ##[29] Snedecor G.W., Cochran W.G., Statistical Methods, Iowa State University Press (1980). ##[30] Soltani A., Rajabi M.H., Zeinali E., Soltani E., Energy Inputs and Greenhouse gases emissions in wheat production in Gorgan, Iran, Energy (2013)50, 5461. ##[31] TaheriRad A., Nikkhah A., Khojastehpour M., Norouzieh S., Assessing the GHG Emissions, the Energy and Economic Analysis of Cotton Production in Golestan Province, The 8th National Congress on Agricultural Machinery Engineering (Biosystem) & Mechanization, 2931 January, Mashhad, Iran (In Farsi)(2014). ##[32] Tzilivakis J., Warner D.J., May M., Lewis K.A., Jaggard K., An Assessment of the Energy Inputs and Greenhouse Gas Emissions in Sugar Beet (Beta vulgaris) Production in the UK Agricultural Systems (2005) 85(2): 101119. ##[33] Khorramdel S., Koocheki A., Nassiri Mahallati M., Khorasani R., Ghorbani R., Evaluation of Carbon Sequestration Potential in Corn Fields with Different Management Systems, Soil and Tillage Research (2013)133, 25e31. ##[34] Yousefi M., Khoramivafa M., Mondani F., Integrated Evaluation of Energy Use, Greenhouse Gas Emissions and Global Warming Potential for Sugar Beet (Beta Vulgaris) Agroecosystems in Iran, Atmospheric Environment (2014)92: 501505. ##[35] Zareiforoush H., Komarizadeh M.H., Alizadeh M.R., Mechanical Properties of Paddy Grains under QuasiStatic Compressive Loading, New York Science Journal (2010)3(7), 4046.##]
A directionalbased branches current method for transmission loss allocation in the poolbased electricity market
2
2
This paper proposes a new method for transmission loss allocation. The share of each bus in the transmission line losses is determined using transmission line loss equations with respect to businjected currents. Then, it is applied to the total network transmission lines. In the proposed method, comparing with other methods, a solution to remove the negative loss allocation has been introduced. This algorithm is based on the electric network relations and the injected power in various buses considering the network topology. The proposed method is studied on a typical threebus network, and applied to the IEEE 14bus networks. In comparison with other methods, a new solution for removing negative loss allocation is proposed.
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177
187


Rahmat
Aazami
Faculty of Engineering, Ilam University, Ilam, Iran
Faculty of Engineering, Ilam University,
Iran
azami.rahmat@yahoo.com


Hassan
Monsef
Faculty of ECE, University of Tehran, Tehran, Iran
Faculty of ECE, University of Tehran, Tehran,
Iran
dra_1364@yahoo.com
Transmission Loss Allocation
Line Losses
Injected Power
Network Topology
[[1] Ilic M., Galiana F., Fink L., Power Systems Restructuring, Engineering and Economics, Norwell, MA, Kluwer(1998). ##[2] Sheblé G.B., Computational Auction Mechanisms for Restructured Power Industry Operation, Norwell, MA, Kluwer (1999). ##[3] Conejo A.J., Arroyo J.M., Guijarro A.L., Transmission Loss Allocation, A Comparison of Different Practical Algorithms, IEEE Trans. Power Systems (2002)17(3): 571576. ##[4] Bialek J.W., Tracing the Flow of Electricity,IEE Proceedings Generation, Transmission and Distribution (1996) 143(4):313320. ##[5] Kirschen D., Strbac G., Tracing Active and Reactive Power between Generators and Loads Using Real and Imaginary Currents, IEEE Transmission Power Systems (1999) 14: 13121319. ##[6] Rau N.S., Radial Equivalents to Map Networks to Market Formats, IEEE Transmission Power Systems (2001) 16(4): 856861. ##[7] Bialek J.W., Tracing the Flow of Electricity,IEE Proceedings Generation, Transmission and Distribution (1996) 143: 313–320. ##[8] Macqueen C. N., Irving M.R., An Algorithm for the Allocation of Distribution System Demand and Energy Losses, IEEE Transmission Power Systems (1996) 11(1): 147–156. ##[9] Kirschen D., Allan R., Strbac G., Contributions of Individual Generators to Loads and Flows, IEEE Transmission Power Systems (1997) 12(1):52–60. ##[10] Tsukamoto Y., Iyoda I., Allocation of Fixed Transmission Cost to Wheeling Transactions by Cooperative Game Theory, IEEE Transmission Power Systems (1996)11(2):620627. ##[11] Chang Y.C., Lu C. N., An Electricity Tracing Method with Application to Power Loss Allocation, International Journal of Electrical Power and Energy Systems (2000)23(1): 1317. ##[12] Gomez Exposito A., Riquelme Santos J.M., Gonzalez Garcia T., Ruiz Velasco E. A., Fair Allocation of Transmission Power Losses, IEEE Transmission Power Systems (2000) 15(1):184–188. ##[13] Galiana F. D., Phelan M., Allocation of Transmission Losses to Bilateral Contracts in a Competitive Environment, IEEE Transmission Power Systems (2000) 15(1):143–150. ##[14] Conejo A. J., Galiana F. D., Kockar I., ZBus Loss Allocation, IEEE Transmission Power Systems (2001) 16: 105110. ##[15] Adsoongnoen C., Ongsakul W., Maurer C., Haubrich H., Transmission Pricing Using the Exact Power and Loss Allocation Method for Bilateral Contracts in a Deregulated Electricity Supply Industry, European Transactions on Electrical Power (2007) 17:240–254 DOI: 10.1002/etep.131. ##[16] Wang H.X., Liu R., Li W.D., Transmission Loss Allocation Based on Circuit Theories and Orthogonal Projection, IEEE Transmission Power Systems (2009) 24(2): 868877. ##[17] Min K.I., Ha S. H., Lee S.W., Moon Y. H., Transmission Loss Allocation Algorithm Using PathIntegral Based on Transaction Strategy, IEEE Transmission Power Systems (2010) 25(1): 195205. ##[18] Li X., Yamashiro S., Wu L., Liu Z., Ouyang M., Generation Scheduling in Deregulated Power Market Taking into Account Transmission Loss Allocation, IET Generation, Transmission & Distribution (2010)4( 7): 883–892. ##[19] Satyaramesh P.V., RadhaKrishna C.,UsageBased Transmission Loss Allocation under Open Access in Deregulated Power Systems, IET Generation, Transmission & Distribution (2010) 4 (11): 1261–1274. ##[20] Kazemi A., Jadid S., Andami H., A Circuit Based Method for Multi Area Transmission Networks Loss Allocation, European Transactions on Electrical Power (2008) 18:753–766 DOI: 10.1002/etep.225.##]
Exergoeconomic analysis and genetic algorithm power optimization of an irreversible regenerative Brayton cycle
2
2
In this study, the performance of an irreversible regenerative Brayton cycle is sought through power maximizations using finitetime thermodynamic concept in finitesize components. Optimizations are performed using a genetic algorithm. In order to take into account the finitetime and finitesize concepts in the current problem, a dimensionless massflow rate parameter is used to deploy time variations. The results of maximum power state optimizations are investigated considering the impact of dimensionless massflow rate parameter variations. One can see that the system performance shows high values of the dimensionless massflow rate parameter because of low power production while the high total cost rate is not reasonable. The other objective (besides power maximization) of the current study is to prepare finitetime thermodynamics for studying more practical systems using new thermodynamic modelling, exergy, and cost analyses of the current system.
1

188
203


Mohammad Mahdi
Naserian
Department of Mechanical Engineering, University of Sistan and Baluchestan, Zahedan, Iran
Department of Mechanical Engineering, University
Iran
mm.naserian@yahoo.com


Said
Farahat
Department of Mechanical Engineering, University of Sistan and Baluchestan, Zahedan, Iran
Department of Mechanical Engineering, University
Iran
said.farahat.usb@gmail.com


Faramarz
Sarhaddi
Department of Mechanical Engineering, University of Sistan and Baluchestan, Zahedan, Iran
Department of Mechanical Engineering, University
Iran
mmahnas@yahoo.com
FiniteTime Thermodynamic
Exergoeconomic
Regenerative Brayton Cycle
Optimization
Maximum Power
[[1] Curzon FL, Ahlborn B., Efficiency of a CarnotEngine at Maximum Power Output, American Journal of Physics (1975)43(1): 22–4. ##[2] Bejan A., Theory of HeatTransfer Irreversible PowerPlants. International Journal of Heat and Mass Transfer (1988)31(6):1211–9. ##[3] Wu C., Power Optimization of a Finite Time Carnot Heat Engine, Energy (1988)13(9): 681–7. ##[4] Gordon JM., Observations on Efficiency of Heat Engines Operating at Maximum Power, American Journal of Physics (1990)58(4): 370–5. ##[5] Wu C, Kiang RL., FiniteTime Thermodynamic Analysis of a Carnot Engine with Internal Irreversibility, Energy (1992) 1(12): 1173–8. ##[6] Cheng CY, Chen CK., Power Optimization of an Endoreversible Regenerative Brayton Cycle, Energy (1996)2(4): 241–7. ##[7] Cheng CY, Chen CK, Power Optimization of an Irreversible Brayton Heat Engine, Energy Sources (1997)1(5): 461–74. ##[8] Chen LG, Sun FR, Wu C, Kiang RL., Theoretical Analysis of the Performance of a Regenerative Closed Brayton Cycle with Internal Irreversibilities, Energy Conversion and Management (1997)3(9): 871–7. ##[9] Bejan A., Thermodynamic Optimization Alternatives, Minimization of Physical Size Subject to Fixed Power, International Journal Energy Research (1999)23: 1111–21. ##[10] Carlos A Herrera, Jairo A Sandoval and Miguel E Rosillo, Power and Entropy Generation of an Extended Irreversible Brayton Cycle, Optimal Parameters and Performance, Journal of Physics D: Applied Physics (2006)39: 3414–3424. ##[11] Wang, C., Chen, L., Ge, Y., Sun, F., Performance Analysis of an Endoreversible Rectangular Cycle with Heat Transfer Loss and Variable Specific Heats of Working Fluid, Journal homepage: www. IJEE. IEE Foundation, (2015) 6(1): 7380. ##[12] Agnew B., Walker S., Ng B., Tam I. C., Finite Time Analysis of a TriGeneration Cycle, Energies (2015) 8(6): 62156229. ##[13] Chen LG, Zheng JL, Sun FR, Wu C., Power Density Analysis and Optimization of a Regenerated Closed VariableTemperature Heat Reservoir Brayton Cycle, Journal of Physics D: Applied Physics (2001) 3(11):1727–39. ##[14] Ust Y, Sahin B, Yilmaz T., Optimization of a Regenerative GasTurbine Cogeneration System Based on a New Exergetic Performance Criterion, Exergetic Performance Coefficient, Proceedings of the Institution of Mechanical Engineers Part A(2007)221:447–58. ##[15] Sadatsakkak S. A., Ahmadi M. H., Bayat R., Pourkiaei S. M., Feidt M., Optimization Density Power and Thermal Efficiency of an Endoreversible Braysson Cycle by Using NonDominated Sorting Genetic Algorithm, Energy Conversion and Management (2015) 93: 3139. ##[16]Acıkkalp E., Exergetic Sustainability Evaluation of Irreversible Carnot Refrigerator, Physica A, Statistical Mechanics and its Applications (2015) 436: 311–320. ##[17] Açıkkalp E., Yamık H., Limits and Optimization of Power Input or Output of Actual Thermal Cycles, Entropy(2013) 15: 3219–3248. ##[18] Ebrahimi R., Effects of Variable Specific Heat Ratio on Performance of an Endoreversible Otto Cycle, Relation (2010)24: 31. ##[19] Madadi V., Tavakoli V., Rahimi A., First and Second Thermodynamic Law Analyses Applied to a Solar Dish Collector, Journal of NonEquilibrium Thermodynamics (2014) 39: 183–197. ##[20] Vaudrey A., Lanzetta F., Feidt M., H. B. Reitlinger and the Origins of the Efficiency at Maximum Power Formula for Heat Engines, Journal of NonEquilibrium Thermodynamics(2014)39: 199–203. ##[21] Açıkkalp E, Yamık H., Modeling and Optimization of Maximum Available Work for Irreversible Gas Power Cycles with Temperature Dependent Specific Heat, Journal of NonEquilibrium Thermodynamics (2015) 40(1):2539. ##[22] Yang B, Chen L G, Ge Y L, Sun F R., Exergy Performance Analyses of an Irreversible TwoStage Intercooled Regenerative Reheated Closed Brayton CHP Plant, International Journal of Exergy (2014)14(4): 459483. ##[23] Yang B, Chen L G, Ge Y L, Sun F R., Finite time exergoeconomic performance of a real, intercooled, regenerated gas turbine cogeneration plant. Part 2: heat conductance distribution and pressure ratio optimization. International Journal of LowCarbon Technologies (2014) 9(4): 262267. ##[24] Zhang Z. L., Chen L. G., Sun F. R., Performance Optimization for Two Classes of Combined Regenerative Brayton and Inverse Brayton Cycles, International Journal of Sustainable Energy (2014) 33(4): 723741. ##[25] Zhang Z. L., Chen L. G., Ge Y. L., Sun F. R., Thermodynamic Analysis for a Regenerative Gas Turbine Cycle in Cooling Process, International Journal of Energy and Environment (2014) 5(6): 701708. ##[26] Yang B., Chen L. G., Ge Y. L., Sun F.R., Exergy Analyses of an Endoreversible Closed Regenerative Brayton Cycle CCHP Plant, International Journal of Energy and Environment (2014) 5(6): 655668. ##[27] Acıkkalp E., Methods Used for Evaluation of Actual Power Generating Thermal Cycles and Comparing Them, International Journal Electrical Power & Energy Systems (2015) 69: 85–89. ##[28] AnguloBrown F., An Ecological Optimization Criterion for FiniteTime Heat Engines, Journal of Applied Physics (1991) 6(11):7465–9. ##[29] Yan Z., Comment on an Ecological Optimization Criterion for FiniteTime Heat Engines, Journal of Applied Physics (1993) 73(7):3583. ##[30] Cheng C.Y., Chen C.K., Ecological Optimization of an Endoreversible Brayton Cycle, Energy Conversion and Management (1998) 3(12):33–44. ##[31] Chen C.Y., Chen C.K., Ecological Optimization of an Irreversible Brayton Heat Engine, Journal Physics D: Applied Physics (1999) 32:350–7. ##[32] Ust, Y., Safa, A., Sahin, B. Ecological Performance Analysis of an Endoreversible Regenerative Brayton HeatEngine, Applied Energy (2005) 80(3): 247260. ##[33] Kumara R., Kaushikb S. C., Kumarc R. Performance Analysis of an Irreversible Regenerative Brayton Cycle Based on Ecological Optimization Criterion, International Journal of Thermal & Environmental Engineering, (2015) 9(1): 2532. ##[34] Long R., Liu W., Ecological Optimization for General Heat Engines, Physica A: Statistical Mechanics and its Applications (2015) 434: 232239. ##[35] Wang J., Chen L., Ge Y., Sun F., Ecological Performance Analysis of an Endoreversible Modified Brayton Cycle, International Journal Sustainable Energy (2014) 33(3): 619634. ##[36] Rio Oliveira S., Scalon V. L., Repinaldo V. P., Ecological Optimization of an Irreversible Brayton Cycle with Regeneration, InterCooling and Reheating, Applied Mathematical Model (2015). ##[37] Naserian M. M., Farahat S., Sarhaddi F. Finite Time Exergy Analysis and MultiObjective Ecological Optimization of a Regenerative Brayton Cycle Considering the Impact of Flow Rate Variations, Energy Conversion and Management (2015) 103: 790800. ##[38] Durmusoglu Y., Ust Y., Thermodynamic Optimization of an Irreversible Regenerative Closed Brayton Cycle Based on Thermoeconomic Performance Criterion, Applied Mathematical Model (2014) 38: 5174–5186. ##[39] Sadatsakkak S. A., Ahmadi M. H., Ahmadi M. A., Thermodynamic and ThermoEconomic Analysis and Optimization of an Irreversible Regenerative Closed Brayton Cycle, Energy Conversion and Management (2015) 94: 124129. ##[40] Qureshi B. A., Zubair S. M., Thermoeconomic Considerations in the Allocation of Heat Transfer Inventory for Irreversible Refrigeration and Heat Pump Systems, International Journal of Refrigeration (2015) 54: 6775. ##[41] Ahmadi M. H., Ahmadi M. A., Bayat R., Ashouri M., Feidt M. ThermoEconomic Optimization of Stirling Heat Pump by Using NonDominated Sorting Genetic Algorithm, Energy Conversion and Management (2015) 91: 315322. ##[42] Qureshi B. A., Thermoeconomic Considerations in the Allocation of Heat Transfer Inventory for Irreversible Power Systems, Applied Thermal Engineering (2015) 90: 305311. ##[43] Sahraie H., Mirani M. R., Ahmadi M. H., Ashouri M., ThermoEconomic and Thermodynamic Analysis and Optimization of a TwoStage Irreversible Heat Pump, Energy Conversion and Management (2015) 99: 8191. ##[44] Dunbar W. R., Lior N. Sources of Combustion Irreversibility, Combustion Science and Technology (1994) 103(16), 4161. ##[45] Bejan A., Tsatsaronis G, Moran M. Thermal Design and Optimization,John Wiley & Sons (1996). ##[46] Deb K., Tushar G., Controlled Elitist NonDominated Sorting Genetic Algorithms for Better Convergence, In Evolutionary MultiCriterion Optimization, Springer Berlin Heidelberg, (2001) 6781. ##[47] Deb K., MultiObjective Optimization Using Evolutionary Algorithms, John Wiley & Sons (2001). ##[48] Seyyedi S. M., Ajam H., Farahat S., A New Approach for Optimization of Thermal Power Plant Based on the Exergoeconomic Analysis and Structural Optimization Method, Application to the CGAM Problem, Energy Conversion and Management (2010) 51(11): 22022211.##]
A feasibility study and economic analysis for application of nanofluids in waste heat recovery
2
2
This paper presents a comprehensive theoretical, experimental, and economic study on the application of nanofluids as heat transfer fluid in waste heat recovery systems. The research work was conducted in a steelmaking complex in which a plate heat exchanger had been used to recover heat from hot process water. The system was theoretically modelled and the effects of using nanofluids as heat transfer fluid were investigated. Nanofluids with ZnO, Al2O3, SiO2, and CuO as nanoparticles and water as base fluid were used in the analysis. It was found that the best performance is obtained with Al2O3 nanofluid. This can increase the effectiveness of the plate heat exchanger by up to four per cent. Based on this analysis, the existing heat transfer fluid (demineralized water) was replaced by Al2O3 nanofluid. The experiment confirmed the theoretically predicted increase of the heat exchanger’s effectiveness but this increase was a little lower than what was expected. Finally, an economic analysis was done using the net present value method. This economic analysis was performed twice: once with local market prices and once with global market prices. The results show that the project is economical based on global market prices.
1

205
214


Mahyar
Ebrahimi
Department of Materials Science and Engineering of Sharif University of Technology, Tehran, Iran
Department of Materials Science and Engineering
Iran


Marzieh
Akhoundi
Faculty of Aerospace Engineering K.N.Toosi University of Technology, Tehran, Iran
Faculty of Aerospace Engineering K.N.Toosi
Iran
m.akhoundi@mail.kntu.ac.ir
Economic analysis
Net Present Value
Heat Recovery Nanofluid
Case Study
Nanofluid
[[1] Murshed S. M. S., Leong K. C., Yang C., Enhanced Thermal Conductivity of TiO2—water Based Nanofluids, International Journal of Thermal Sciences (2005) 44(4): 367373. ##[2] Patel H. E., Sundararajan T., Das S. K., An Experimental Investigation into the Thermal Conductivity Enhancement in Oxide and Metallic Nanofluids, Journal of Nanoparticle Research, (2010) 12(3): 10151031. ##[3] Chandrasekar M., Suresh S., Chandra Bose A., Experimental Investigations and Theoretical Determination of Thermal Conductivity and Viscosity of Al2O3/Water Nanofluid, Experimental Thermal and Fluid Science (2010)34(2): 210216. ##[4] Vajjha R. S., Das D. K., Namburu P. K., Numerical Study of Fluid Dynamic and Heat Transfer Performance of Al2O3 and CuO Nanofluids in the Flat Tubes of a Radiator, International Journal of Heat and Fluid Flow (2010) 31(4): 613621. ##[5] Duangthongsuk W., Wongwises S., Measurement of TemperatureDependent Thermal Conductivity and Viscosity of TiO2Water Nanofluids, Experimental Thermal and Fluid Science (2009)33(4): 706714. ##[6] Sundar L. S., Singh M. K., Sousa A., Enhanced Heat Transfer and Friction Factor of MWCNT–Fe3O4/Water Hybrid Nanofluids, International Communications in Heat and Mass Transfer (2014) 52: 7383. ##[7] Hussain S. H., Hussein A. K., Natural Convection Heat Transfer Enhancement in a Differentially Heated Parallelogrammic Enclosure Filled With CopperWater Nanofluid. Journal of Heat Transfer (2014) 136(8): 082502. ##[8] Wen D., Ding Y., Experimental Investigation into Convective Heat Transfer of Nanofluids at the Entrance Region under Laminar Flow Conditions, International Journal of Heat and Mass Transfer (2004) 47(24): 51815188. ##[9] Rea U., McKrell T., Hu L. W., Buongiorno J., Laminar Convective Heat Transfer and Viscous Pressure Loss of Alumina–Water and Zirconia–Water Nanofluids, International Journal of Heat and Mass Transfer (2009) 52(7): 20422048. ##[10] Kulkarni D. P., Das D. K., Vajjha R. S., Application of Nanofluids in Heating Buildings and Reducing Pollution, Applied Energy (2009) 86(12): 25662573. ##[11] Rennie T. J., Raghavan V. G., Numerical Studies of a DoublePipe Helical Heat Exchanger, Applied Thermal Engineering (2006) 26(11): 12661273. ##[12] KazemiBeydokhti A., Zeinali Heris S., Thermal Optimization of Combined Heat and Power (CHP) Systems Using Nanofluids, Energy (2012) 44(1): 241247. ##[13] Wang L., Sundén B., Manglik R. M., Plate Heat Exchangers, Design, Applications and Performance (Eds.), (2007) 11. ##[14] Hesselgreaves J. E., Compact Heat Exchangers, Selection, Design and Operation. Gulf Professional Publishing (2001). ##[15] Shah R. K., Sekulic D. P. Fundamentals of Heat Exchanger Design, John Wiley & Sons (2003). ##[16] Wang L., Sunden B., Optimal Design of Plate Heat Exchangers with and without Pressure Drop Specifications, Applied Thermal Engineering (2003) 23(3): 295311. ##[17] Guo Z. Y., Liu X. B., Tao W. Q., Shah R. K., Effectiveness–Thermal Resistance Method for Heat Exchanger Design and Analysis, International Journal of Heat and Mass Transfer (2010) 53(13): 28772884. ##[18] Vlasogiannis P., Karagiannis G., Argyropoulos P., Bontozoglou V., Air–Water TwoPhase Flow and Heat Transfer in a Plate Heat Exchanger, International Journal of Multiphase Flow (2002) 28(5):757772. ##[19] Galeazzo F. C., Miura R. Y., Gut J. A., Tadini C. C., Experimental and Numerical Heat Transfer in a Plate Heat Exchanger. Chemical Engineering Science (2006) 61(21): 71337138. ##[20] Bassiouny M. K., Martin H., Flow Distribution and Pressure Drop in Plate Heat Exchangers—I UType Arrangement, Chemical Engineering Science (1984) 39(4): 693700. ##[21] Bassiouny M. K., Martin H., Flow Distribution and Pressure Drop in Plate Heat Exchangers—II ZType Arrangement. Chemical Engineering Science (1984) 39(4):701704. ##[22] Tsai Y. C., Liu F. B., Shen P. T., Investigations of the Pressure Drop and Flow Distribution in a ChevronType Plate Heat Exchanger, International Communications in Heat and Mass Transfer (2009) 36(6): 574578. ##[23] Zhang Z., Li Y., CFD Simulation on Inlet Configuration of PlateFin Heat Exchangers, Cryogenics (2003) 43(12): 673678. ##[24] Prabhakara Rao B., Krishna Kumar P., Das S. K., Effect of Flow Distribution to the Channels on the Thermal Performance of a Plate Heat Exchanger, Chemical Engineering and Processing, Process Intensification (2002) 41(1): 4958. ##[25] Li X. W., Meng J. A., Li Z. X., An Experimental Study of the Flow and Heat Transfer between Enhanced Heat Transfer Plates for PHEs, Experimental Thermal and Fluid Science (2010) 34(8): 11941204. ##[26] Durmuş A., Benli H., Kurtbaş İ., Gül H., Investigation of Heat Transfer and Pressure Drop in Plate Heat Exchangers Having Different Surface Profiles, International Journal of Heat and Mass Transfer (2009) 52(5):14511457. ##[27] Muley A., Manglik R. M., Experimental Study of Turbulent Flow Heat Transfer and Pressure Drop in a Plate Heat Exchanger with Chevron Plates, Journal of Heat Transfer (1999) 121(1): 110117. ##[28] Dović D., Palm B., Švaić S., Generalized Correlations for Predicting Heat Transfer and Pressure Drop in Plate Heat Exchanger Channels of Arbitrary Geometry, International Journal of Heat and Mass Transfer (2009) 52(19): 45534563. ##[29] Khan T. S., Khan M. S., Chyu M. C., Ayub Z. H. Experimental Investigation of Single Phase Convective Heat Transfer Coefficient in a Corrugated Plate Heat Exchanger for Multiple Plate Configurations, Applied Thermal Engineering (2010) 30(8): 10581065. ##[30] Tinaut F. V., Melgar A., Ali A. A. Correlations for Heat Transfer and Flow Friction Characteristics of Compact PlateType Heat Exchangers, International Journal of Heat and Mass Transfer (1992) 35(7): 16591665. ##[31] http://www.alfalaval.com/campaigns/wasteheatrecovery/heatexchangers/pages/heatexchangers.aspx ##[32] Choi S. U., Zhang Z. G., Keblinski P. Nanofluids, In Encyclopedia of Nanoscience and Nanotechnology (2004) 6 (773):757773. ##[33] Pak B. C., Cho Y. I. Hydrodynamic and Heat Transfer Study of Dispersed Fluids with Submicron Metallic Oxide Particles, Experimental Heat Transfer an International Journal (1998) 11(2): 151170. ##[34] Corcione M. Heat Transfer Features of BuoyancyDriven Nanofluids inside Rectangular Enclosures Differentially Heated at the Sidewalls, International Journal of Thermal Sciences (2010) 49(9): 15361546. ##[35] Xuan Y., Roetzel W. Conceptions for Heat Transfer Correlation of Nanofluids, International Journal of Heat and Mass Transfer (2000) 43(19): 37013707. ##[36] Patel H. E., Sundararajan T., Das S. K., An Experimental Investigation into the Thermal Conductivity Enhancement in Oxide and Metallic Nanofluids, Journal of Nanoparticle Research (2010)12(3): 10151031. ##[37] Xuan Y., Li Q., Investigation on Convective Heat Transfer and Flow Features of Nanofluids, Journal of Heat Transfer (2003) 125(1): 151155.##]
Power injection of renewable energy sources using modified model predictive control
2
2
This paper presents a simple model predictive control (MPC) approach to control the power injection system (PIS) for renewable energy applications. A DC voltage source and a singlephase inverter that is connected to the grid by an LCL filter form the PIS. Grid voltage is considered a disturbance for the system. For eliminating this disturbance, a modified model is proposed. It is usual to control output current to inject a desired power to grid. But due to the presence of the LCL filter, we face a thirdorder system and other states should be bounded during operation. In this work, we ensure the stability of other state variables and, consequently, system stability, by defining a proper cost function. In this regard, reference signals are calculated for all state variables. For getting the benefit of the switching nature of the inverter, we use a finite control set model predictive control (FCSMPC). Proposed predictive control is implemented in a digital scheme and, thereby, the discrete model of the system is extracted. The proposed controller does not require any other control loop or modulation method. Simulation results show the effective performance of the proposed control scheme.
1

215
224


Seyed Seraj
Hamidi
Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran
Department of Electrical and Robotic Engineering,
Iran
seyedseraj.hamidi@gmail.com


Hossein
GholizadeNarm
Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran
Department of Electrical and Robotic Engineering,
Iran
gholizade@shahroodut.ac.ir
Finite Control Set Model Predictive Control
Power Injection System (PIS)
LCL Filter
Voltage Source Inverter (VSI)
Renewable Energy
[[1] Monfared M., Golestan S., Control Strategies for SinglePhase Grid Integration of SmallScale Renewable Rnergy Sources, A Review, Renewable and SustainableEnergy Reviews (2012) 16 (7): 49824993. ##[2] Mantilla M. A., Petit J., Ordonez G., Rincon D., Sierra O., Control of Three Phase Inverters for Renewable Energy Systems Under Unbalanced Grid Voltages, International Journal of Renewable Energy Research (IJRER) (2015) 5 (2): 507516. ##[3] Chakraborty A., Advancements in Power Electronics and Drives in Interface with Growing Renewable Energy Resources, Renewable and Sustainable Energy Reviews (2011) 15 (4): 1816–1827. ##[4] Hassaine L., OLias E., Quintero J., Salas V., Overview of Power Inverter Topologies and Control Structures for Grid Connected Photovoltaic Systems, Renewable and Sustainable Energy Reviews (2014) 30 (0): 796–807. ##[5] Zhong Q.C., Hornik T., Control of Power Inverters in Renewable Energy and Smart Grid Integration, WileyIEEE Press (2013) ISBN 9780470667095. ##[6] Wang T. C. Y., Ye Z., Sinha G., Yuan X., Output Filter Design for a GridInterconnected ThreePhase Inverter, Proceedings of the 2003 IEEE 34th Annual Power Electronics Specialist Conference (PESC ’03) (2003) 779–784. ##[7] Twining E., Holmes D. G., Grid current Regulation of a ThreePhase Voltage Source Inverter with an LCL Input Filter, IEEE Transactions on Power Electronics (2003) 18 (3): 888–895. ##[8] Zeng Z., Yang H., Zhao R., Cheng C., Topologies and Control Strategies of MultiFunctional GridConnected Inverters for Power Quality Enhancement, A Comprehensive Review, Renewable and Sustainable Energy Reviews (2013) 24 (0): 223–270. ##[9] Hojabri M., Ahmad A. Z., Toudeshki A., Soheilirad M., An Overview on Current Control Techniques for Grid Connected Renewable Energy Systems, International Proceedings of Computer Science and Information Technology (2012) 119126. ##[10] Teodorescu R., Blaabjerg F., Liserre M., Loh P. C., ProportionalResonant Controllers and Filters for GridConnected VoltageSource Converters, IEE Proceedings  Electric Power Applications (2006) 153 (5): 750–762. ##[11] Jung S., Bae Y., Choi S., Kim H., A Low Cost Utility Interactive Inverter for Residential Fuel Cell Generation, IEEE Transactions on Power Electronics (2007) 22 (6):2293–2298. ##[12] Shen G., Xu D., Cao L., Zhu X., An Improved Control Strategy for GridConnected Voltage Source Inverters With an LCL Filter, IEEE Transactions on Power Electronics (2008) 23 (4): 1899–1906. ##[13] GholizadeNarm H., A Novel Control Strategy for a Singlephase Gridconnected Power Injection System, International Journal of EngineeringTransactions C: Aspects (2014) 27 (12): 1841–1849. ##[14] Eren S., Pahlevaninezhad M., Bakhshai A., Jain P. K., Composite Nonlinear Feedback Control and Stability Analysis of a GridConnected Voltage Source Inverter With LCL Filter, IEEE Transactions on Industrial Electronics (2013) 60 (11):5059–5074. ##[15] Komurcugil H., Ozdemir S., Sefa I., Altin N., Kukrer O., SlidingMode Control for SinglePhase GridConnected LCLFiltered VSI with DoubleBand Hysteresis Scheme, IEEE Transactions on Industrial Electronics (2016) 63 (2): 864–873. ##[16] Kouro S., Cortes P., Vargas R., Ammann U., Rodriguez J., Model Predictive Control  A Simple and Powerful Method to Control Power Converters, IEEE Transactions on Industrial Electronics (2009) 56 (6): 1826–1838. ##[17] Vazquez S., Leon J. I., Franquelo L. G., Rodriguez J., Young H. A., Marquez A., Zanchetta P., Model Predictive Control, A Review of Its Applications in Power Electronics, IEEE Industrial Electronics Magazine (2014) 8 (1):16–31. ##[18] Bordons C., Montero C., Basic Principles of MPC for Power Converters: Bridging the Gap Between Theory and Practice, IEEE Industrial Electronics Magazine (2015) 9 (3):31–43. ##[19] Mariethoz S., Morari M., Explicit ModelPredictive Control of a PWM Inverter with an LCL Filter, IEEE Transactions on Industrial Electronics (2009) 56 (2):389–399. ##[20] Rodriguez J., Pontt J., Silva C. A., Correa P., Lezana P., Cortes P., Ammann U., Predictive Current Control of a Voltage Source Inverter, IEEE Transactions on Industrial Electronics (2007) 54 (1):495–503. ##[21] Cortes P., Kazmierkowski M. P., Kennel R. M., Quevedo D. E., Rodriguez J., Predictive Control in Power Electronics and Drives, IEEE Transactions on Industrial Electronics (2008) 55 (12): 4312–4324. ##[22] Rodriguez J., Cortes P., Predictive Control of Power Converters and Electrical Drives, WileyIEEE Press (2012) ISBN 9781119963981. ##[23] Aguilera R. P., Quevedo D. E., On Stability and Performance of Finite Control set MPC for Power Converters, Proceedings of the 2011 Workshop on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) (2011) 55–62. ##[24]Rojas C. A., Yuz J. I., Aguirre M., Rodriguez J., A Comparison of DiscreteTime Models for Model Predictive Control of Induction Motor Drives, Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT) (2015) 568–573. ##[25]Silva C. A., Yuz J. I., On SampledData Models for Model Predictive Control, IECON 2010  36th Annual Conference on IEEE Industrial Electronics Society (2010) 2966–2971. ##[26]Ogata K., Discretetime Control Systems, (2nd ed), Prentice Hall Englewood Cliffs, NJ (1995) ISBN 9780130342812. ##[27]Lamchich M. R. T., Average Current Mode Control of a Voltage Source Inverter Connected to the Grid, Application to Different Filter Cells, Journal of Electrical Engineering (2004) 55 (34):77–82.##]
Exergoenvironmental and exergoeconomic analyses and multicriteria optimization of a novel solardriven CCHP based on Kalina cycle
2
2
The present research proposes and optimizes the performance of a novel solardriven combined cooling, heating, and power (CCHP) Kalina system for two seasons—winter and summer—based on exergy, exergoeconomic, and exergoenvironmental concepts applying a Nondominated Sort Genetic AlgorithmII (NSGAII) technique. Three criteria, i.e. daily exergy efficiency, total product cost rate, and total product environmental impact rate associated with the exergy of the system for each season are considered simultaneously for multiobjective optimization. The outcomes reveal that increments in turbine inlet pressure and mass flow rate of the vapour generator lower the environmental impact of system products as well as the total product cost rate in both seasons. The optimum value of daily exergy efficiency, total product environmental impact rate, and total product cost rate indicate improvements by 2.56%, 15.7%, and 15.3% respectively in summer and 36.34%, 7.39%, and 4.93% respectively in winter, relative to the base point.
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225
244


Mona
Rahmatian
Department of Mechanical Engineering, Faculty of Engineering & Technology, Alzahra University, Tehran, Iran
Department of Mechanical Engineering, Faculty
Iran
mona.rahatian@gmail.com


Fateme
Ahmadi Boyaghchi
Department of Mechanical Engineering, Faculty of Engineering & Technology, Alzahra University, Tehran, Iran
Department of Mechanical Engineering, Faculty
Iran
fahmadi@alzahra.ac.ir
CCHP
Exergy analysis
ExergoEconomic Analysis
ExergoEnvironmental Analysis
multiobjective optimization
[[1] Kalina A.I., Generation of Energy by Means of a Working Fluid, and Regeneration of a Working Fluid (1982) ##[2] Nag P.K., Gupta A.V.S.S.K.S., Exergy Analysis of the Kalian Cycle, Applied Thermal Engineering (1998) 427439. ##[3] Xinxin Z., Maogang H., Ying Z., A Review of Research on the Kalian Cycle, Renewable and Sustainable Energy Reviews (2012) 53095318. ##[4] Kalina A., Leibowitz H., Application of the Kalina Cycle Technology to Geothermal Power Generation, Geothermal Resources Council Transactions (1989)p. 11605. ##[5] Hettiarachchi H., Golubovic M., Worek W., The Performance of the Kalina Cycle System 11 (KCS11) with LowTemperature Heat Sources, Journal Energy Resour Techno (2007) 243247. ##[6] Nasruddin, et al., Energy and Exergy Analysis of Kalina Cycle System (KCS) 34 with Mass Fraction AmmoniaWater Mixture Variation, Mechanical Science and Technology (2009)18711876. ##[7] Lolos P.A., Rogdakis E.D., Thermodynamic Analysis Of A Kalina Power Unit Driven By Low Temperature Heat Sources. Thermal science (2009) 13: 2131. ##[8] Sun F., Ikegami Y., Jia B., A Study on Kalina Solar System with an Auxiliary Superheater, Renewable Energy (2012) 41: 210219. ##[9] Shankar Ganesh N., Srinivas T., Optimized Kalina Cycle, in Frontiers in Automobile and Mechanical Engineering (FAME)(2010). ##[10] Wang J., et al., Parametric Analysis and Optimization of a Kalina Cycle Driven by Solar Energy. Applied Thermal Engineering (2013)50(1): 408415. ##[11] Li X., Zhang Q., Li,X., A Kalina Cycle with Ejector, Energy (2013) 54(0): 212219. ##[12] Xu F., Yogi Goswami D., Bhagwat S. S., A Combined Power/Cooling Cycle. Energy (2000) 25(3): 233246. ##[13] Tamm G., et al., Theoretical and Experimental Investigation of an Ammonia–Water Power and Refrigeration Thermodynamic Cycle, Solar Energy (2004) 76(1): p. 217228. ##[14] Martin C., Goswami D.Y., Effectiveness of Cooling Production with a Combined Power and Cooling Thermodynamic Cycle, Applied Thermal Engineering, (2006) 26(5–6): 576582. ##[15] Padilla R.V., et al., Analysis of Power and Cooling Cogeneration Using AmmoniaWater Mixture, Energy (2010) 35(12): 46494657. ##[16] Demirkaya G., et al., Analysis of a Combined Power and Cooling Cycle for LowGrade Heat Sources, Energy Research (2010) 35: 11451157. ##[17] Jawahar C., et al., Simulation Studies on Gax Based Kalina Cycle for Both Power and Cooling Applications, Applied Thermal Engineering (2011). ##[18] Zare V., et al., Thermoeconomic Analysis and Optimization of an Ammonia–Water Power/Cooling Cogeneration Cycle,Energy (2012). ##[19] Ma S., et al., Thermodynamic Analysis of a New Combined Cooling, Heat and Power System Driven by Solid Oxide Fuel Cell Based on Ammonia–Water Mixture, Journal of Power Sources, (2011) 196(20): 84638471. ##[20] Meyer L., et al., Exergoenvironmental Analysis for Evaluation of the Environmental Impact of Energy Conversion Systems, Energy(2009) 34(1): 7589. ##[21] Boyano A., et al., Exergoenvironmental Analysis of a Steam Methane Reforming Process for Hydrogen Production, Energy (2011) 36(4): 22022214. ##[22] Petrakopoulou F., et al., Exergoeconomic and Exergoenvironmental Analyses of a Combined Cycle Power Plant with Chemical Looping Technology, International Journal of Greenhouse Gas Control (2011)5(3): 475482. ##[23] Petrakopoulou F., et al., Exergoeconomic and Exergoenvironmental Evaluation of Power Plants Including CO2 Capture, Chemical Engineering Research and Design (2011) 89(9): 14611469. ##[24] Atılgan R., et al., Environmental Impact Assessment of a Turboprop Engine with the Aid of Exergy, Energy (2013) 58: 664671. ##[25] Abusoglu A., M.S. Sedeeq, Comparative Exergoenvironmental Analysis and Assessment of Various Residential Heating Systems, Energy and Buildings, (2013) 62: 268277. ##[26] BlancoMarigorta A.M., Masi M., Manfrida G., ExergoEnvironmental Analysis of a Reverse Osmosis Desalination Plant in Gran Canaria, Energy (2014)76: 223232. ##[27] Hamut H., Dincer I., Naterer G., Exergoenvironmental Analysis of Hybrid Electric Vehicle Thermal Management Systems, Journal of Cleaner Production (2014)67187196. ##[28] Khoshgoftar Manesh, M., et al., Exergoeconomic and Exergoenvironmental Evaluation of the Coupling of a Gas Fired Steam Power Plant with a Total Site Utility System. Energy Conversion and Management, (2014)77: 469483. ##[29] Keçebaş A., Exergoenvironmental Analysis for a Geothermal District Heating System, An Application, Energy, (2016)94: 391400. ##[30] Fergani Z., Touil D., Morosuk T., MultiCriteria Exergy Based Optimization of an Organic Rankine Cycle for Waste Heat Recovery in the Cement Industry, Energy Conversion and Management (2016)112: 8190. ##[31] Mosaffa A., Farshi L.G., Exergoeconomic and Environmental Analyses of an Air Conditioning System Using Thermal Energy Storage, Applied Energy(2016) 162: 515526. ##[32] Kalogirou S.A., Solar Energy Engineering, Processes and Systems. (2009). ##[33] Bejan A., Moran M.J., Thermal Design and Optimization (1996). ##[34] Li. H., et al., Performance Characteristics of R1234yf EjectorExpansion Refrigeration Cycle, Applied Energy, (2014)121: 96103. ##[35] Wang J., Dai Y., Sun Z., A Theoretical Study on a Novel Combined Power and Ejector Refrigeration Cycle, International Journal of Refrigeration (2009)32(6): 11861194. ##[36] Çengel Y.A., Boles M.A., Thermodynamics: an Engineering Approach, McGrawHill Higher Education (2006). ##[37] Ogriseck S., Integration of Kalina Cycle in a Combined Heat and Power Plant, A Case Study, Applied Thermal Engineering (2009) 29(14–15): 28432848. ##[38] Index M.S.E.C., Economic Indicators. Chemical engineering, September (2013) ##[39] Ahmadi P., Dincer I., Rosen M.A., MultiObjective Optimization of a Novel SolarBased Multigeneration Energy System. Solar Energy (2014)108: 576591. ##[40] Smith R.M., Chemical Process, Design and Integration(2005). ##[41] Zhou C., Doroodchi E., Moghtaderi B., An inDepth Assessment of Hybrid Solar–Geothermal Power Generation, Energy Conversion and Management, (2013)74: 88101. ##[42] Campos Rodríguez C.E., et al., Exergetic and Economic Comparison of ORC and Kalina Cycle for Low Temperature Enhanced Geothermal System in Brazil, Applied Thermal Engineering (2012). ##[43] ElEmam R.S., Dincer I., Exergy and Exergoeconomic Analyses and Optimization of Geothermal Organic Rankine Cycle, Applied Thermal Engineering(2013)59(1): 435444. ##[44] Petrakopoulou F., et al., Environmental Evaluation of a Power Plant Using Conventional and Advanced ExergyBased Methods, Energy(2012)45(1): 2330. ##[45] Boyano A., et al., Exergoenvironmental Analysis of a Steam Methane Reforming Process for Hydrogen Production, Energy(2011)36(4): 22022214. ##[46] Kanoglu, M., Bolatturk A., Performance and Parametric Investigation of a Binary Geothermal Power Plant by Exergy. Renewable Energy(2008) 33(11): 23662374. ##[47] Van Gool W., Energy Policy, Fairy Tales and Factualities, in Innovation and Technology—Strategies and Policies, Springer (1997) 93105. ##[48] Solver., E.E.E., Available at: http://www.fchart.com/. ##[49] PL Y., MultipleCriteria Decision Making, Concepts, Techniques, and Extensions, Springer Science & Business Media (2013).##]
The optimum pressure for working fluid in feed water heaters of steam power plants
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2
The aim of this study is to find the optimal water pressure and percentage of supply vapour in the feed water heaters (fwhs) of steam power plants, such that they maximize the thermal efficiency of the Rankine cycle within prespecified values of minimum and maximum pressures of the thermodynamic cycle. Thermal efficiency is defined as a function of unknown variables (fluid pressure and vapour percentage of each fwh), and it is maximized numerically using the nonlinear constraint optimization method. Precise values of enthalpy are used in computations of thermal efficiency during the nonlinear optimization process. The enthalpy and entropy values at different points of the thermodynamic cycle are calculated utilizing the industrial formulation of IAPWSIF97.
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253


Alireza
Pourshaghaghy
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering,
Iran
apoursh@gmail.com
Steam Power Plant
Feed Water Heater
Optimum Pressure
[[1] Srinivas T., Gupta A., Reddy B.V., Generalized Thermodynamic Analysis of Steam Power Cycles with Number of Feed Water Heaters, International Journal of Thermodynamics (2007) 10 (4):177185. ##[2] Bejan A., Advanced Engineering Thermodynamics, Third Edition, Wiley, (2006). ##[3] ElWakil M.M., Power Plant Technology, McGrawHill (2002). ##[4] Burghardt M.D., Harbach J.A., Engineering Thermodynamics, Fourth Edition, Harper Collins College (1993). ##[5] http://users.encs.concordia.ca/~kadem/Rankine_regenerative%20cycle.pdf (2015). ##[6] Cao L., Wang J., Dai Y., Thermodynamic Analysis of a BiomassFired Kalina Cycle with Regenerative Heater, Energy(2014) 77: 760770. ##[7] Sengupta S., Datta A., Duttagupta S., Exergy Analysis of a CoalBased 210MW Thermal Power Plant, International Journal of Energy Research (2007) 31:14–28. ##[8] Gupta M.K., Kaushik S.C., Exergy Analysis and Investigation for Various Feed Water Heaters of Direct Steam Generation Solar–Thermal Power Plant, Renewable Energy (2010) 35:1228–1235. ##[9] Moghadassi A.R. , Parvizian F., Abareshi B., Azari F., Alhajri I., Optimization of Regenerative Cycle with Open Feed Water Heater Using Genetic Algorithms and Neural Networks, Journal of Thermal Analysis and Calorimetry (2010) 100: 757–761. ##[10] Farhad S., SaffarAvval M., YounessiSinaki M., Efficient Design of Feedwater Heaters Network in Steam Power Plants Using Pinch Technology and Exergy Analysis, International Journal of Energy Research (2008) 32:1–11. ##[11] Akolekara H.D, Srinivasan P., Challa J.S., Development of a Simulation Program to Optimise Process Parameters of Steam Power Cycles, International Journal of Thermal & Environmental Engineering (2014) 8 (1):5561. ##[12] Ventura C.A.M., Rowlands A.S., Recuperated Power Cycle Analysis Model, Investigation and Optimisation of LowtoModerate Resource Temperature Organic Rankine Cycles, Energy (2015) 93: 484494. ##[13] Le V.L., Feidt M., Kheiri A., Pelloux Prayer S., Performance Optimization of LowTemperature Power Generation by Supercritical ORCs (Organic Rankine Cycles) Using Low GWP (global warming potential) Working Fluids, Energy (2014) 67:513526. ##[14] Pourshaghaghy A., Calculation of Some Thermodynamic Properties of Water for the boundary points between region 3 and 4 of industrial formulation, Journal of Engineering Studies and Research (2015) 21 (1):6675. ##[15] http://www.iapws.org/ (2016).##]
Application of ANFIS and linear regression models to analyze the energy and economics of lentil and chickpea production in Iran
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2
In the present study, the energetic and economic modeling of lentil and chickpea production in Esfahan province of Iran was conducted using adaptive neurofuzzy inference system (ANFIS) and linear regression. Data were taken by interviewing and visiting of 140 lentil farms and 110 chickpea farms during 20142015 production period. The results showed that the yield and total energy consumption were calculated 2,023 kgha1 and 32,970.10 MJha1, respectively for lentil; and 2,276 kg ha1 and 33,211.18 MJ ha1, respectively for chickpea. Energy use efficiency was found to be 0.9 for lentil and 1.02 for chickpea; while benefitcost ratio (BCR) were obtained 1.60 for lentil and 1.74 for chickpea. Regression results demonstrated that the coefficient of determination (R2) were 0.92 for lentil and 0.89 for chickpea. In adittion, in regression estimated model in terms of BCR, R2 were obtained as 0.86 for lentil and 0.72 for chickpea. In modeling of yield using the best ANFIS model, R2 were calculated 0.99 and 0.98, respectively for lentil and chickpea. Finally, for evaluation of crops BCR by best ANFIS model, R2 were determinate as 0.94 and 0.91 for lentil and chickpea, respectively. It was concluded that ANFIS model could better predict the energy output and BCR than that of linear regression model.
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255
270


Behzad
Elhami
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Department of Agricultural Machinery Engineering,
Iran


Asadollah
Akram
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Department of Agricultural Machinery Engineering,
Iran
aakram@ut.ac.ir


Majid
Khanali
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Department of Agricultural Machinery Engineering,
Iran
khanali@ut.ac.ir


Seyed Hashem
MousaviAvval
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Department of Agricultural Machinery Engineering,
Iran
sh.mousavi@ut.ac.ir
Energy Use Efficiency
Linear Regression
prediction
CobbDouglas Model
[[1] Torres J., Rutherfurd S.M., Muñoz LS, Peters M., Montoya CA., The impact of heating and soaking on the in vitro enzymatic hydrolysis of protein varies in different species of tropical legumes, Food Chemistry, 194, (2016) 377–382. ##[2] Food and Agricultural Organization (FAO), (2008), www.fao.org. ##[3] Anonymous. Annual agricultural statistics, Ministry of Jihade Agriculture of Iran, (2014), www.maj.ir. ##[4] Rafiee S., Mousavi Avval S.H., Mohammadi A., Modeling and sensitivity analysis of energy inputs for apple production in Iran, Energy, 35, (2010) 33013306. ##[5] Alam M.S., Alam M.R., Islam K.K., Energy flow in agriculture: Bangladesh, American Journal of Environment Science, 1(3), (2005) 213320. ##[6] Zangeneh M., Omid M., Akram A., A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran, Energy, 35, (2010) 29272933. ##[7] Mohammadi A., Omid M., Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran, Applied Energy, 87, (2010) 191–196. ##[8] Thankappan S., Midmore P., Jenkins T., Conserving energy in small holder agriculture: a multiobjective programming casestudy of northwest India, Ecological Economics, 56, ( 2005) 190208. ##[9] Baruah D.C., Dutta P.K., An investigation into the energy use in relation to yield of rice (Oryza sativa) in Assam, India. Agriculture, Ecosystems and Environment, 120, (2007) 185191. ##[10] Mohammadi A., Rafiee S., Mohtasebi S.S., Rafiee H., Energy inputsyield relationship and cost analysis of kiwifruit production in Iran, Renewable Energy, 35, (2010) 10711075. ##[11] MousaviAvval S.H., Rafiee S., Jafari A., Mohammadi A., Energy flow modeling and sensitivity analysis of inputs for canola production in Iran, Journal of Cleaner Production, 19, (2011) 14641470. ##[12] Ghasemi Mobtaker H., Keyhani A., Mohammadi A., Rafiee S., Akram A., Sensitivity Analysis of Energy Inputs for Barley Production in Hamedan Province of Iran, Agriculture, Ecosystems and Environment (2010) 137:367372. ##[13] Khoshnevisan B., Rafiee S., Omid M., Mousazadeh H., Prediction of Potato Yield Based on Energy Inputs Using MultiLayer Adaptive NeuroFuzzy Inference System, Measurement (2014) 47:521–530. ##[14] Cheng C.B., Cheng C.J., Lee E.S., NeuroFuzzy and Genetic Algorithm in Multiple Response Optimization. Computers and Mathematics with Applications (2002) 44:1503–1514. ##[15] AlGhandoor A., Phelan P.E., Villalobos R., Phelan B.E., Manufacturing Aggregate Energy Intensity Decomposition, The Application of Multivariate Regression Analysis, International Journal Energy Research (2008) 32: 501–513. ##[16] Sefeedpari P., Rafiee S., Akram A., Pishgar Komleh S.H., Modeling Output Energy Based on Fossil Fuels and Electricity Energy Consumption on Dairy Farms of Iran, Application of Adaptive neuralfuzzy inference system technique, Computers and Electronics in Agriculture (2014) 109: 80–85. ##[17] Naderloo L., Alimardani R., Omid M., Sarmadian F., Javadikia P., Torabi M.Y., Alimardani F., Application of ANFIS to Predict Crop Yield Based on Different Energy Inputs, Measurement (2012) 45:1406– 1413. ##[18] Statistical Yearbook of Esfahan Province in Iran (amar.org.ir/english/IranStatisticalYearbook) (2013). ##[19] Banaeian N., Omid M., Ahmadi H., Energy and Economic Analysis of Greenhouse Strawberry Production in Tehran Province of Iran, Energy Conversion and Management (2010) 52: 1020–1025. ##[20] Kitani O., Energy and Biomass Engineering. In, CIGR Handbook of Agricultural Engineering, St. Joseph, MI, ASAE (1999) 330. ##[21] PishgarKomleh S.H., Keyhani A., MostofiSarkari M.R., Jafari A., Energy and Economic Analysis of Different Seed Corn Harvesting Systems in Iran, Energy (2012) 43: 469476. ##[22] Singh G., Singh S., Singh J., Optimization of Energy Inputs for Wheat Crop in Punjab, Energy Converse Management (2004) 45: 453–465. ##[23] Hatrili S.A., Ozkan B., Fert C., Energy Inputs and Crop Yield Relationship in Greenhouse Tomato Production, Renewable Energy (2006) 31: 427–438. ##[24] Ghasemi Mobtaker H., Akram A., Keihani A., Economic Modeling and Sensitivity Analysis of the Cost Inputs for Alfalfa Production in Iran, A Case Study from Hamedan Province, Ocean Journal of Applied Sciences (2010) 3: 313319. ##[25] Ubeyli E.D., Adaptive NeuroFuzzy Inference System Employing Wavelet Coefficients for Detection of Ophthalmic Arterial Disorders, Expert Systems with (2008) 34: 2201–2209. ##[26] Singh R., Kainthola A., Singh T.N., Estimation of Elastic Constant of Rocks Using an ANFIS Approach, Applied Soft Computing (2012) 12: 40–45. ##[27] Khoshnevisan B., Rafiee S., Omid M., Yousefi M., Movahedi M., Modeling of Energy Consumption and GHG (greenhouse gas) Emissions in Wheat Production in Esfahan Province of Iran Using Artificial Neural Networks, Energy (2013a) 52: 333338. ##[28] Safa M., Samarasinghe S., Determination and Modeling of Energy Consumption in Wheat Production Using Neural Networks, A Case Study in Canterbury Province, New Zealand, Energy (2011) 36: 51405147. ##[29] Koocheki A., Ghorbani R., Monadi F., Alizadeh Y., Moradi R., Pulses Production Systems in Term of Energy Use Efficiency and Economical Analysis in Iran, International Journal of Energy Economics and Policy (2011) 4(1): 95106. ##[30] Patil S.L., Mishra P.K., Loganandhan N., Ramesha M.N., Math S.K.N., Energy, Economics and Water Use Efficiency of Chickpea (Cicer arietinum L.) Cultivars in Vertisols of SemiArid Tropics, Indian Machineries Research Communication, (2014) 107:656664. ##[31] Yousefi M., Damghani A.M., Evaluation of Energy Flow and Indicators of Chickpea under Rainfed Condition in Iran, International Journal of Farming and Allied Sciences (2012) 1(2): 57 61. ##[32] Tabatabaie S.M.H., Rafiee S., Keyhani A., Ebrahimi A.H., Energy and Economic Assessment of Prune Production in Tehran Province of Iran, Journal of Cleaner Production (2013) 39: 280284. ##[33] Zhang L.X., Song B., Chen B., EmergyBased Analysis of Four Farming Systems, Insight into Agricultural Diversification in Rural China, Journal of Cleaner Production (2012) 28: 3344. ##[34] Mohammadshirazi A., Akram A., Rafiee S., MousaviAvval S.H., Bagheri Kalhor E., An Analysis of Energy Use and Relation between Energy Inputs and Yield in Tangerine Production, Renewable and Sustainable Energy Reviews (2012) 16:4515–4521. ##[35] Cetin C., Vardar A., An Economic Analysis of Energy Requirements and Input Costs for Tomato Production in Turkey, Renewable Energy (2008) 33: 428433. ##[36]Khoshnevisan B., Rafiee S., Mousazadeh H., Environmental Impact Assessment of Open Field and Greenhouse Strawberry Production, European Journal Agronomy (2013b) 50: 2937.##]
Optimization of the PCMintegrated solar domestic hot water system under different thermal stratification conditions
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2
Many researchers have investigated how to increase the overall efficiency of solardriven thermal systems. Several key parameters, such as collector efficiency and storage tank characteristics, may impose some constraints on the annual solar fraction (ASF) of such systems. In this paper, the behaviour of integrating the phase change material (PCM) in SDHW systems is modelled and optimized numerically. Coupled collector and partly stratified PCMembedded storage tank governing equations are utilized to simulate the overall performance of the system. The developed code presents the monthly behaviour of the system including the solar fraction and the storage tank temperature profile. The results indicate that the stratification of the storage tank will increase the ASF up to about 4.6%. Additionally, it is found that the optimum amount for the PCM and its melting temperature is changed as the tank stratification goes from the fully mixed to the fully stratified state. Integrating the PCM in the storage tank leads to increases of 5.3% in the ASF for a singlenode tank, while a rise of only 0.7% is seen for the stratified storage tank.
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271
279


Mehrdad
Shirinbakhsh
Mechanical Engineering Department, K.N. Toosi University of Technology Tehran, Tehran, Iran
Mechanical Engineering Department, K.N. Toosi
Iran
mehrdad.shirinbakhsh@gmail.com


Nima
Mirkhani
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Mechanical Engineering, College
Iran
s.n.mirkhani@gmail.com


Behrang
Sajadi
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Mechanical Engineering, College
Iran
bsajadi@ut.ac.ir
Solar Domestic Hot Water System
Phase Change Material
Stratification
Annual Solar Fraction
Optimization
[[1] Thirugnanasambandam M., Iniyan S., Goic R., A Review of Solar Thermal Technologies, Renewable and Sustainable Energy Reviews (2010) 14: 312–322. ##[2] Shukla A., Buddhi D., Sawhney R.L., Solar Water Heaters with Phase Change Material Thermal Energy Storage Medium, A Review, Renewable and Sustainable Energy Reviews (2009) 13: 2119–2125. ##[3] Sharma A., Chen C.R., Solar Water Heating System with Phase Change Materials, International Review of Chemical Engineering (I. RE. CH. E.) (2009) 1: 297–307. ##[4] Shukla R., Sumathy K., Erickson P., Gong J., Recent Advances in the Solar Water Heating Systems, A Review, Renewable and Sustainable Energy Reviews (2013) 19:173–190. ##[5] Sharma S.D., Sagara K., Latent Heat Storage Materials and Systems, A Review, International Journal of Green Energy (2005) 2: 1–56. ##[6] Haillot D., Nepveu F., Goetz V., Py X., Benabdelkarim M., High Performance Storage Composite for the Enhancement of Solar Domestic Hot Water Systems, Part 2, Numerical System Analysis, Solar Energy (2012) 86: 64–77. ##[7] Padovan R., Manzan M., Genetic Optimization of a PCM Enhanced Storage Tank for Solar Domestic Hot Water Systems, Solar Energy (2014) 103: 563–573. ##[8] Mather D.W., Hollands K.G.T., Wright J.L., Singleand MultiTank Energy Storage for Solar Heating Systems, Fundamentals, Solar Energy (2002) 73: 3–13. ##[9] Rhee J., Campbell A., Mariadass A., Morhous B., Temperature Stratification from Thermal Diodes in Solar Hot Water Storage Tank, Solar Energy (2010) 84: 507–511. ##[10] Andersen E., Furbo S., Fan J., Multilayer Fabric Stratification Pipes for Solar Tanks, Solar Energy (2007) 81: 1219–1226. ##[11] Haillot D., Franquet E., Gibout S., Bédécarrats J.P., Optimization of Solar DHW System Including PCM Media, Applied Energy (2013) 109: 470–475. ##[12] Duffie J., Beckman W., Worek W.M., Solar Engineering of Thermal Processes, 2nd Edition, 1994. DOI:10.1115/1.2930068. ##[13] Sharma A., Tyagi V. V, Chen C.R., Buddhi D., Review on Thermal Energy Storage with Phase Change Materials and Applications, Renewable and Sustainable Energy Reviews (2009) 13: 318–345. ##[14] Khalifa A. J. N., Jabbar R. A. A.,Conventional Versus Storage Domestic Solar Hot Water Systems, A Comparative Performance Study, Energy Conversion and Management (2010) 51: 265–270. ##[15] Nkwetta D.N., Vouillamoz P.E., Haghighat F., ElMankibi M., Moreau A., Daoud A., Impact of Phase Change Materials Types and Positioning on Hot Water Tank Thermal Performance, Using Measured Water Demand Profile, Applied Thermal Engineering (2014) 67:460–468.##]
A thermodynamic model for exergetic performance and optimization of a solar and biomassfuelled multigeneration system
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2
Integrated energy systems utilizing renewable sources are sustainable and environmentally substitutes for conventional fossilfired energy systems. A new multigeneration plant with two inputs, such as biomass and solar energy, and four useful outputs, such as cooling, heating, power, and distilled water, is presented and investigated in this paper. The proposed system includes evacuated tube solar collectors, biomass burners, the organic rankine cycle (ORC), absorption chillers, heaters, and a multieffect desalination system (MED). The results showed that the proposed system can produce 802.5 kW for power, 10391 kW for heating, 5658 kW for cooling, and 9.328 kg/s for distilled water. The energy efficiency of the system is 61%, while the exergy efficiency is 7% and the main sources of exergy destructions are biomass burner, evacuated tube solar collectors, and the vapour generator. Exergy optimization is carried out to find the optimum point of the system.
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281
289


Alireza
Noorpoor
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
Graduate Faculty of Environment, University
Iran
noorpoor@ut.ac.ir


Parisa
Heidarnejad
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
Graduate Faculty of Environment, University
Iran


Nasim
Hashemian
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
Graduate Faculty of Environment, University
Iran


Amir
Ghasemi
Graduate Faculty of Environment, University of Tehran, Tehran, Iran
Graduate Faculty of Environment, University
Iran
Multigeneration
Desalination
Optimization
Solar energy
Biomass
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